Introduction

The purpose of this project is to predict the final rankings in the 2024 World Figure Skating Championships for the Men’s and Women’s Single categories. Figure skating is an Olympic sport where the athlete performs individually, in pairs, or in groups on ice on figure skates. The Olympic disciplines are men’s single, women’s singles, pair skating, and ice dance, and those are also split within various levels from beginner to senior elite. For this project, we will focus on the senior elite level for men’s and women’s singles, since they will compete at the World Championships and have the same scoring styles, unlike pair skating and ice dance.

Figure skating competitions at the elite level are held over 2 days where skaters are expected to perform 2 programs, the short program and the free skate (also referred to as the free program), and earn scores for both programs. Their final score is based on the sum of the 2 scores.

Yuzuru Hanyu - Men’s Figure Skating
Yuzuru Hanyu - Men’s Figure Skating

Why?

Typically, fans of the sport might predict the rankings of the World Championships based on a skater’s top score or average score of the current season. However, the problem with that is that each event has different sets of judges who may have different consistencies in scoring. A perfect skate at one event can be judged to be full of mistakes at another. Presentation and artistry are also evaluated, which could be subjective. The location of the event could also have an effect on the performance of a skater, like higher elevation could cause poorer performances. Therefore, the average scores of a competition could vary a lot and a winning score at one event could receive a lower score at another.

So, I wanted to see how the variation between competition scores could skew our ideas of the scores during the World Championships!

Data Description

All scores from International Skating Union recognized competitions are posted on their website. However it can be messy and have varying formats, so I used SkatingScores, which has the scores in clean, organized tables.

Within a regular figure skating season, there are 9 major ISU events leading up to Worlds: 6 Grand Prix events, a Grand Prix Final where the top 6 skaters during the 6 GP events get to compete, the European Championships, and the 4 Continents Championships. During a Winter Olympic year, the Olympics serve as an extra competition.

No skater will compete at all 9 events. 4 Continents is a competition for nations not part of Europe, so no skater will be competing in both the 4 Continents and European Championships. Grand Prix events are by invite only and skaters can only attend 2 at most. At most, a skater can attend 4 of these competitions or 5 for an Olympic year. At the least but rarely, a skater could not compete at any. So there could be potential missing data there, which we will look into.

I am using data from the 2016-2017, 2017-18, 2018-19, 2021-22, and 2022-23 competitive seasons. I skipped 2019-20 because the World Championships were cancelled due to COVID-19. I also skipped 2020-21 because a lot of events were cancelled also due to COVID, giving me insufficient data. The 2021-22 also did not have a Grand Prix Final event because of COVID, but otherwise there is a sufficient amount of data from that season.

Here is the regular score dataset, where I merged all of the scores from the 9/10 regular competitions over the mentioned seasons. I also added columns specifying the sex of the skater and the competition and the year of the score. I selected the year based on what year the corresponding World Championship competition occurs.

#merge regular scores
reg_merged <- dplyr::bind_rows(list(score23, score22, score19, score18, score17), .id = 'year')

#add year column
reg_merged <- reg_merged %>% 
  clean_names() %>%
  mutate(year = ifelse(year == 1, 2023,
                ifelse(year == 2, 2022,
                ifelse(year == 3, 2019, 
                ifelse(year == 4, 2018, 2017)))))
#clean skaters
reg_merged$skater <- gsub('[0-9]', '', reg_merged$skater)
reg_merged$skater <- str_squish(reg_merged$skater)

reg_merged %>% 
  kable() %>% 
  kable_styling(full_width = F) %>% 
  scroll_box(width = "100%", height = "200px")
year final_rank skater nation sp_score sp_rank fs_score fs_rank total_score competition sex
2023 1 Kaori Sakamoto JP 71.72 1 145.89 1 217.61 GPUSA Female
2023 2 Isabeau Levito US 71.30 2 135.36 2 206.66 GPUSA Female
2023 3 Amber Glenn US 68.42 3 129.19 3 197.61 GPUSA Female
2023 4 Haein Lee KR 66.24 4 113.26 5 179.50 GPUSA Female
2023 5 Ekaterina Kurakova PL 63.65 6 115.03 4 178.68 GPUSA Female
2023 6 Gracie Gold US 64.18 5 109.91 6 174.09 GPUSA Female
2023 7 Nicole Schott DE 56.47 10 103.88 8 160.35 GPUSA Female
2023 8 Yeonjeong Park KR 60.04 7 98.54 9 158.58 GPUSA Female
2023 9 Ahsun Yun KR 47.98 11 108.72 7 156.70 GPUSA Female
2023 10 Eliska Brezinova CZ 56.65 9 96.92 10 153.57 GPUSA Female
2023 11 Marilena Kitromilis CY 46.01 12 89.47 11 135.48 GPUSA Female
2023 NA Rino Matsuike JP 59.50 8 NA NA NA GPUSA Female
2023 1 Ilia Malinin US 86.08 4 194.29 1 280.37 GPUSA Male
2023 2 Kao Miura JP 94.96 1 178.23 2 273.19 GPUSA Male
2023 3 Junhwan Cha KR 94.44 2 169.61 3 264.05 GPUSA Male
2023 4 Daniel Grassl IT 88.43 3 169.25 4 257.68 GPUSA Male
2023 5 Roman Sadovsky CA 78.15 5 147.26 7 225.41 GPUSA Male
2023 6 Wesley Chiu CA 71.58 9 148.32 6 219.90 GPUSA Male
2023 7 Liam Kapeikis US 74.29 8 145.21 8 219.50 GPUSA Male
2023 8 Sena Miyake JP 77.87 6 137.87 9 215.74 GPUSA Male
2023 9 Koshiro Shimada JP 62.54 12 152.58 5 215.12 GPUSA Male
2023 10 Dinh Tran US 64.99 11 134.69 10 199.68 GPUSA Male
2023 11 Mihhail Selevko EE 75.75 7 116.05 12 191.80 GPUSA Male
2023 12 Donovan Carrillo MX 69.18 10 119.10 11 188.28 GPUSA Male
2023 1 Yelim Kim KR 72.22 1 132.27 2 204.49 GPJPN Female
2023 2 Kaori Sakamoto JP 68.07 2 133.80 1 201.87 GPJPN Female
2023 3 Rion Sumiyoshi JP 68.01 3 125.11 4 193.12 GPJPN Female
2023 4 Audrey Shin US 65.87 4 123.13 5 189.00 GPJPN Female
2023 5 Rinka Watanabe JP 58.36 9 129.71 3 188.07 GPJPN Female
2023 6 Seoyeon Ji KR 62.92 6 121.22 7 184.14 GPJPN Female
2023 7 Niina Petrõkina EE 58.81 8 121.48 6 180.29 GPJPN Female
2023 8 Seoyeong Wi KR 61.06 7 115.68 10 176.74 GPJPN Female
2023 9 Starr Andrews US 64.13 5 109.93 12 174.06 GPJPN Female
2023 10 Olga Mikutina AT 56.95 10 116.41 9 173.36 GPJPN Female
2023 11 Amber Glenn US 52.04 11 117.32 8 169.36 GPJPN Female
2023 12 Eva Lotta Kiibus EE 48.56 12 113.81 11 162.37 GPJPN Female
2023 1 Shoma Uno JP 91.66 2 188.10 1 279.76 GPJPN Male
2023 2 Sota Yamamoto JP 96.49 1 161.36 6 257.85 GPJPN Male
2023 3 Junhwan Cha KR 80.35 6 174.41 2 254.76 GPJPN Male
2023 4 Kazuki Tomono JP 85.07 4 166.76 3 251.83 GPJPN Male
2023 5 Adam Siao Him Fa FR 87.44 3 163.01 4 250.45 GPJPN Male
2023 6 Matteo Rizzo IT 78.57 7 162.19 5 240.76 GPJPN Male
2023 7 Nika Egadze GE 84.47 5 148.39 8 232.86 GPJPN Male
2023 8 Stephen Gogolev CA 69.01 9 152.01 7 221.02 GPJPN Male
2023 9 Gabriele Frangipani IT 68.78 10 143.53 9 212.31 GPJPN Male
2023 10 Conrad Orzel CA 73.10 8 129.59 11 202.69 GPJPN Male
2023 11 Maurizio Zandrón AT 68.21 11 133.51 10 201.72 GPJPN Male
2023 12 Tomoki Hiwatashi US 57.18 12 127.87 12 185.05 GPJPN Male
2023 1 Mai Mihara JP 72.23 1 145.20 1 217.43 GPGBR Female
2023 2 Isabeau Levito US 72.06 2 143.68 2 215.74 GPGBR Female
2023 3 Anastasiia Gubanova GE 66.82 3 126.29 5 193.11 GPGBR Female
2023 4 Young You KR 61.21 6 130.15 3 191.36 GPGBR Female
2023 5 Ekaterina Kurakova PL 63.46 4 126.98 4 190.44 GPGBR Female
2023 6 Nicole Schott DE 60.38 7 121.03 6 181.41 GPGBR Female
2023 7 Gabriella Izzo US 62.92 5 111.18 7 174.10 GPGBR Female
2023 8 Gabrielle Daleman CA 58.95 8 104.82 8 163.77 GPGBR Female
2023 9 Alexia Paganini CH 54.63 11 102.26 10 156.89 GPGBR Female
2023 10 Julia Sauter RO 52.38 12 104.08 9 156.46 GPGBR Female
2023 11 Natasha Mckay GB 57.62 9 97.58 11 155.20 GPGBR Female
2023 12 Bradie Tennell US 56.50 10 96.69 12 153.19 GPGBR Female
2023 1 Daniel Grassl IT 86.85 2 177.50 1 264.35 GPGBR Male
2023 2 Deniss Vasiljevs LV 83.01 3 171.55 2 254.56 GPGBR Male
2023 3 Shun Sato JP 82.68 4 166.35 3 249.03 GPGBR Male
2023 4 Koshiro Shimada JP 80.84 5 166.33 4 247.17 GPGBR Male
2023 5 Tatsuya Tsuboi JP 76.75 7 149.38 5 226.13 GPGBR Male
2023 6 Roman Sadovsky CA 89.49 1 129.86 8 219.35 GPGBR Male
2023 7 Jimmy Ma US 77.72 6 136.75 7 214.47 GPGBR Male
2023 8 Morisi Kvitelashvili GE 56.42 12 138.83 6 195.25 GPGBR Male
2023 9 Tomoki Hiwatashi US 66.68 8 122.05 9 188.73 GPGBR Male
2023 10 Corey Circelli CA 62.97 10 119.84 10 182.81 GPGBR Male
2023 11 Graham Newberry GB 64.30 9 116.12 12 180.42 GPGBR Male
2023 12 Edward Appleby GB 62.52 11 117.61 11 180.13 GPGBR Male
2023 1 Loena Hendrickx BE 72.75 1 143.59 1 216.34 GPFRA Female
2023 2 Yelim Kim KR 68.93 2 125.83 4 194.76 GPFRA Female
2023 3 Rion Sumiyoshi JP 64.10 5 130.24 3 194.34 GPFRA Female
2023 4 Haein Lee KR 62.77 6 130.72 2 193.49 GPFRA Female
2023 5 Audrey Shin US 64.27 4 119.66 5 183.93 GPFRA Female
2023 6 Mana Kawabe JP 68.83 3 113.67 8 182.50 GPFRA Female
2023 7 Rino Matsuike JP 57.68 9 118.84 6 176.52 GPFRA Female
2023 8 Maé-Bérénice Méité FR 58.84 8 116.84 7 175.68 GPFRA Female
2023 9 Léa Serna FR 62.63 7 105.26 9 167.89 GPFRA Female
2023 10 Olga Mikutina AT 56.00 10 103.99 10 159.99 GPFRA Female
2023 11 Lindsay Van Zundert NL 55.11 11 98.98 11 154.09 GPFRA Female
2023 12 Maïa Mazzara FR 46.05 12 94.80 12 140.85 GPFRA Female
2023 1 Adam Siao Him Fa FR 88.00 3 180.98 1 268.98 GPFRA Male
2023 2 Sota Yamamoto JP 92.42 1 165.48 3 257.90 GPFRA Male
2023 3 Kazuki Tomono JP 89.46 2 159.31 4 248.77 GPFRA Male
2023 4 Sihyeong Lee KR 76.54 7 166.08 2 242.62 GPFRA Male
2023 5 Nika Egadze GE 82.44 4 150.96 6 233.40 GPFRA Male
2023 6 Luc Economides FR 77.23 6 152.41 5 229.64 GPFRA Male
2023 7 Lukas Britschgi CH 74.25 9 148.61 7 222.86 GPFRA Male
2023 8 Ivan Shmuratko UA 75.95 8 144.13 8 220.08 GPFRA Male
2023 9 Mihhail Selevko EE 79.40 5 133.52 11 212.92 GPFRA Male
2023 10 Wesley Chiu CA 67.95 11 142.00 10 209.95 GPFRA Male
2023 11 Landry Le May FR 60.87 12 142.52 9 203.39 GPFRA Male
2023 NA Sena Miyake JP 69.27 10 NA NA NA GPFRA Male
2023 1 Mai Mihara JP 73.58 2 130.56 1 204.14 GPFIN Female
2023 2 Loena Hendrickx BE 74.88 1 129.03 3 203.91 GPFIN Female
2023 3 Mana Kawabe JP 67.03 3 130.38 2 197.41 GPFIN Female
2023 4 Rika Kihira JP 64.07 6 128.36 4 192.43 GPFIN Female
2023 5 Madeline Schizas CA 65.19 5 122.65 5 187.84 GPFIN Female
2023 6 Lindsay Thorngren US 65.75 4 117.48 6 183.23 GPFIN Female
2023 7 Anastasiia Gubanova GE 56.03 9 110.54 8 166.57 GPFIN Female
2023 8 Bradie Tennell US 60.64 7 103.34 9 163.98 GPFIN Female
2023 9 Jenni Saarinen FI 59.69 8 95.95 11 155.64 GPFIN Female
2023 10 Janna Jyrkinen FI 42.89 12 111.56 7 154.45 GPFIN Female
2023 11 Linnea Ceder FI 55.63 10 96.28 10 151.91 GPFIN Female
2023 12 Eva Lotta Kiibus EE 49.27 11 89.62 12 138.89 GPFIN Female
2023 1 Ilia Malinin US 85.57 2 192.82 1 278.39 GPFIN Male
2023 2 Shun Sato JP 81.59 3 180.62 2 262.21 GPFIN Male
2023 3 Kévin Aymoz FR 88.96 1 166.73 3 255.69 GPFIN Male
2023 4 Tatsuya Tsuboi JP 78.82 5 166.08 4 244.90 GPFIN Male
2023 5 Camden Pulkinen US 72.45 7 157.47 5 229.92 GPFIN Male
2023 6 Nikolaj Majorov SE 69.94 8 139.61 6 209.55 GPFIN Male
2023 7 Arlet Levandi EE 72.67 6 136.83 7 209.50 GPFIN Male
2023 8 Keegan Messing CA 80.12 4 124.90 12 205.02 GPFIN Male
2023 9 Valtter Virtanen FI 69.15 9 134.87 8 204.02 GPFIN Male
2023 10 Aleksandr Selevko EE 66.96 11 132.51 10 199.47 GPFIN Male
2023 11 Lucas Tsuyoshi Honda JP 67.92 10 129.98 11 197.90 GPFIN Male
2023 12 Morisi Kvitelashvili GE 62.42 12 134.38 9 196.80 GPFIN Male
2023 1 Mai Mihara JP 74.58 2 133.59 1 208.17 GPF Female
2023 2 Isabeau Levito US 69.26 5 127.97 2 197.23 GPF Female
2023 3 Loena Hendrickx BE 74.24 3 122.11 4 196.35 GPF Female
2023 4 Rinka Watanabe JP 72.58 4 123.43 3 196.01 GPF Female
2023 5 Kaori Sakamoto JP 75.86 1 116.70 6 192.56 GPF Female
2023 6 Yelim Kim KR 61.55 6 119.03 5 180.58 GPF Female
2023 1 Shoma Uno JP 99.99 1 204.47 1 304.46 GPF Male
2023 2 Sota Yamamoto JP 94.86 2 179.49 3 274.35 GPF Male
2023 3 Ilia Malinin US 80.10 5 191.84 2 271.94 GPF Male
2023 4 Shun Sato JP 76.62 6 173.54 4 250.16 GPF Male
2023 5 Kao Miura JP 87.07 3 158.67 6 245.74 GPF Male
2023 6 Daniel Grassl IT 80.40 4 164.57 5 244.97 GPF Male
2023 1 Rinka Watanabe JP 63.27 6 134.32 1 197.59 GPCAN Female
2023 2 Starr Andrews US 64.69 5 126.57 2 191.26 GPCAN Female
2023 3 Young You KR 65.10 4 125.05 4 190.15 GPCAN Female
2023 4 Ava Marie Ziegler US 66.49 3 120.27 7 186.76 GPCAN Female
2023 5 Rika Kihira JP 59.27 8 125.06 3 184.33 GPCAN Female
2023 6 Niina Petrõkina EE 61.68 7 119.66 8 181.34 GPCAN Female
2023 7 Madeline Schizas CA 67.90 1 112.69 9 180.59 GPCAN Female
2023 8 Yuhana Yokoi JP 54.87 12 123.86 5 178.73 GPCAN Female
2023 9 Lindsay Thorngren US 55.16 10 120.93 6 176.09 GPCAN Female
2023 10 Gabrielle Daleman CA 66.65 2 104.96 11 171.61 GPCAN Female
2023 11 Lindsay Van Zundert NL 55.22 9 105.74 10 160.96 GPCAN Female
2023 12 Eliska Brezinova CZ 55.14 11 103.89 12 159.03 GPCAN Female
2023 1 Shoma Uno JP 89.98 2 183.17 1 273.15 GPCAN Male
2023 2 Kao Miura JP 94.06 1 171.23 2 265.29 GPCAN Male
2023 3 Matteo Rizzo IT 81.18 3 169.85 4 251.03 GPCAN Male
2023 4 Keegan Messing CA 79.69 4 171.03 3 250.72 GPCAN Male
2023 5 Camden Pulkinen US 75.07 5 143.99 8 219.06 GPCAN Male
2023 6 Lukas Britschgi CH 64.35 8 148.08 6 212.43 GPCAN Male
2023 7 Stephen Gogolev CA 57.94 11 152.70 5 210.64 GPCAN Male
2023 8 Aleksandr Selevko EE 60.37 10 145.74 7 206.11 GPCAN Male
2023 9 Jimmy Ma US 61.73 9 142.66 9 204.39 GPCAN Male
2023 10 Deniss Vasiljevs LV 69.01 7 128.44 10 197.45 GPCAN Male
2023 11 Conrad Orzel CA 69.69 6 125.73 11 195.42 GPCAN Male
2023 1 Anastasiia Gubanova GE 69.81 1 130.10 1 199.91 EC Female
2023 2 Loena Hendrickx BE 67.85 2 125.63 3 193.48 EC Female
2023 3 Kimmy Repond CH 63.83 3 128.68 2 192.51 EC Female
2023 4 Ekaterina Kurakova PL 61.81 5 125.09 4 186.90 EC Female
2023 5 Nina Pinzarrone BE 61.35 6 124.57 5 185.92 EC Female
2023 6 Niina Petrõkina EE 61.05 7 122.69 6 183.74 EC Female
2023 7 Janna Jyrkinen FI 60.77 8 116.19 7 176.96 EC Female
2023 8 Lara Naki Gutmann IT 55.39 13 113.90 8 169.29 EC Female
2023 9 Nicole Schott DE 54.33 16 109.49 9 163.82 EC Female
2023 10 Julia Sauter RO 56.58 11 103.84 12 160.42 EC Female
2023 11 Sofja Stepchenko LV 55.32 14 104.02 11 159.34 EC Female
2023 12 Olga Mikutina AT 62.78 4 96.30 18 159.08 EC Female
2023 13 Marilena Kitromilis CY 53.71 18 105.20 10 158.91 EC Female
2023 14 Lindsay Van Zundert NL 58.13 10 99.97 15 158.10 EC Female
2023 15 Eva Lotta Kiibus EE 55.26 15 101.69 13 156.95 EC Female
2023 16 Alexandra Feigin BG 54.31 17 100.92 14 155.23 EC Female
2023 17 Josefin Taljegård SE 55.53 12 99.45 16 154.98 EC Female
2023 18 Livia Kaiser CH 60.25 9 90.95 20 151.20 EC Female
2023 19 Natasha Mckay GB 51.94 20 96.06 19 148.00 EC Female
2023 20 Anastasia Gozhva UA 46.78 22 96.91 17 143.69 EC Female
2023 21 Daša Grm SI 52.47 19 90.58 21 143.05 EC Female
2023 22 Mia Caroline Risa Gomez NO 49.14 21 88.48 22 137.62 EC Female
2023 23 Júlia Láng HU 46.33 23 83.95 23 130.28 EC Female
2023 24 Nikola Rychtaříková CZ 45.64 24 77.49 24 123.13 EC Female
2023 25 Alexandra Michaela Filcová SK 43.94 25 NA NA NA EC Female
2023 26 Léa Serna FR 43.93 26 NA NA NA EC Female
2023 27 Antonina Dubinina RS 42.51 27 NA NA NA EC Female
2023 28 Anastasia Gracheva MD 39.08 28 NA NA NA EC Female
2023 29 Alexandra Mintsidou GR 33.86 29 NA NA NA EC Female
2023 1 Adam Siao Him Fa FR 96.53 1 171.24 2 267.77 EC Male
2023 2 Matteo Rizzo IT 86.46 2 173.46 1 259.92 EC Male
2023 3 Lukas Britschgi CH 79.26 5 168.75 3 248.01 EC Male
2023 4 Kévin Aymoz FR 83.75 4 157.17 4 240.92 EC Male
2023 5 Deniss Vasiljevs LV 84.81 3 151.54 6 236.35 EC Male
2023 6 Daniel Grassl IT 77.03 8 153.80 5 230.83 EC Male
2023 7 Nika Egadze GE 72.96 12 147.69 7 220.65 EC Male
2023 8 Mihhail Selevko EE 73.74 11 144.56 8 218.30 EC Male
2023 9 Andreas Nordebäck SE 75.98 9 136.97 10 212.95 EC Male
2023 10 Gabriele Frangipani IT 77.35 7 134.27 12 211.62 EC Male
2023 11 Maurizio Zandrón AT 72.57 13 135.11 11 207.68 EC Male
2023 12 Tomás Guarino Sabaté ES 71.65 14 133.54 13 205.19 EC Male
2023 13 Mark Gorodnitsky IL 64.94 22 137.40 9 202.34 EC Male
2023 14 Valtter Virtanen FI 68.33 18 129.95 14 198.28 EC Male
2023 15 Nikita Starostin DE 74.70 10 123.27 17 197.97 EC Male
2023 16 Morisi Kvitelashvili GE 70.55 16 124.04 16 194.59 EC Male
2023 17 Vladimir Samoilov PL 78.26 6 113.33 21 191.59 EC Male
2023 18 Adam Hagara SK 65.15 21 124.57 15 189.72 EC Male
2023 19 Jari Kessler HR 67.87 19 114.96 19 182.83 EC Male
2023 20 Burak Demirboga TR 64.33 23 118.49 18 182.82 EC Male
2023 21 Kyrylo Marsak UA 70.41 17 111.57 22 181.98 EC Male
2023 22 Davidé Lewton Brain MC 66.07 20 113.47 20 179.54 EC Male
2023 23 Graham Newberry GB 70.85 15 103.79 24 174.64 EC Male
2023 24 Aleksandr Vlasenko HU 62.49 24 111.45 23 173.94 EC Male
2023 25 Petr Kotlarik CZ 60.24 25 NA NA NA EC Male
2023 26 Georgiy Reshtenko CZ 54.52 26 NA NA NA EC Male
2023 27 Larry Loupolover BG 53.26 27 NA NA NA EC Male
2023 28 Samuel Mcallister IE 48.07 28 NA NA NA EC Male
2023 29 David Sedej SI 46.28 29 NA NA NA EC Male
2023 1 Haein Lee KR 69.13 6 141.71 1 210.84 4CC Female
2023 2 Yelim Kim KR 72.84 1 136.45 3 209.29 4CC Female
2023 3 Mone Chiba JP 67.28 7 137.70 2 204.98 4CC Female
2023 4 Chaeyeon Kim KR 71.39 3 131.00 5 202.39 4CC Female
2023 5 Rinka Watanabe JP 65.60 8 134.90 4 200.50 4CC Female
2023 6 Bradie Tennell US 69.49 5 130.42 6 199.91 4CC Female
2023 7 Amber Glenn US 69.63 4 122.87 8 192.50 4CC Female
2023 8 Hana Yoshida JP 59.82 10 129.78 7 189.60 4CC Female
2023 9 Sara-Maude Dupuis CA 51.68 12 118.99 9 170.67 4CC Female
2023 10 Madeline Schizas CA 60.11 9 99.62 10 159.73 4CC Female
2023 11 Justine Miclette CA 51.24 13 98.32 11 149.56 4CC Female
2023 12 Jocelyn Hong NZ 52.02 11 96.31 12 148.33 4CC Female
2023 13 Tzu-Han Ting TW 45.19 17 95.32 13 140.51 4CC Female
2023 14 Tara Prasad IN 46.04 14 87.11 14 133.15 4CC Female
2023 15 Joanna So HK 45.90 15 87.09 15 132.99 4CC Female
2023 16 Anna Levkovets KZ 45.53 16 83.73 16 129.26 4CC Female
2023 17 Sofia Farafonova KZ 44.66 18 81.82 17 126.48 4CC Female
2023 18 Vlada Vasiliev AU 40.13 21 73.82 18 113.95 4CC Female
2023 19 Cheuk Ka Kahlen Cheung HK 39.58 22 64.80 19 104.38 4CC Female
2023 20 Hiu Yau Chow HK 42.10 20 58.06 21 100.16 4CC Female
2023 21 Bagdana Rakhishova KZ 35.96 23 59.62 20 95.58 4CC Female
2023 NA Isabeau Levito US 71.50 2 NA NA NA 4CC Female
2023 NA Sofia Lexi Jacqueline Frank PH 43.82 19 NA NA NA 4CC Female
2023 1 Kao Miura JP 91.90 1 189.63 1 281.53 4CC Male
2023 2 Keegan Messing CA 86.70 2 188.87 2 275.57 4CC Male
2023 3 Shun Sato JP 80.81 6 178.33 3 259.14 4CC Male
2023 4 Junhwan Cha KR 83.77 5 166.37 4 250.14 4CC Male
2023 5 Mikhail Shaidorov KZ 72.43 12 164.71 5 237.14 4CC Male
2023 6 Sihyeong Lee KR 70.38 14 157.41 6 227.79 4CC Male
2023 7 Boyang Jin CN 85.32 4 142.15 10 227.47 4CC Male
2023 8 Conrad Orzel CA 80.09 7 146.01 7 226.10 4CC Male
2023 9 Jimmy Ma US 86.64 3 134.40 13 221.04 4CC Male
2023 10 Maxim Naumov US 75.96 8 142.75 9 218.71 4CC Male
2023 11 Koshiro Shimada JP 74.06 10 143.79 8 217.85 4CC Male
2023 12 Jaeseok Kyeong KR 75.30 9 136.68 12 211.98 4CC Male
2023 13 Stephen Gogolev CA 72.82 11 136.94 11 209.76 4CC Male
2023 14 Liam Kapeikis US 71.43 13 126.57 14 198.00 4CC Male
2023 15 Yudong Chen CN 67.93 15 116.48 16 184.41 4CC Male
2023 16 Dias Jirenbayev KZ 57.67 18 125.75 15 183.42 4CC Male
2023 17 Edrian Paul Célestino PH 66.83 16 100.09 19 166.92 4CC Male
2023 18 Darian Kaptich AU 58.22 17 107.91 17 166.13 4CC Male
2023 19 Rakhat Bralin KZ 54.25 19 105.95 18 160.20 4CC Male
2023 20 Pagiel Yie Ken Sng SG 48.52 20 90.31 20 138.83 4CC Male
2023 21 Douglas Gerber NZ 40.75 21 85.20 21 125.95 4CC Male
2023 22 Lap Kan Lincoln Yuen HK 36.33 22 80.71 22 117.04 4CC Male
2022 1 Anna Shcherbakova RU 80.20 2 175.75 2 255.95 OLY Female
2022 2 Alexandra Trusova RU 74.60 4 177.13 1 251.73 OLY Female
2022 3 Kaori Sakamoto JP 79.84 3 153.29 3 233.13 OLY Female
2022 4 Kamila Valieva RU 82.16 1 141.93 5 224.09 OLY Female
2022 5 Wakaba Higuchi JP 73.51 5 140.93 6 214.44 OLY Female
2022 6 Young You KR 70.34 6 142.75 4 213.09 OLY Female
2022 7 Alysa Liu US 69.50 8 139.45 7 208.95 OLY Female
2022 8 Loena Hendrickx BE 70.09 7 136.70 9 206.79 OLY Female
2022 9 Yelim Kim KR 67.78 9 134.85 11 202.63 OLY Female
2022 10 Mariah Bell US 65.38 11 136.92 8 202.30 OLY Female
2022 11 Anastasiia Gubanova GE 65.40 10 135.58 10 200.98 OLY Female
2022 12 Ekaterina Kurakova PL 59.08 24 126.76 12 185.84 OLY Female
2022 13 Viktoriia Safonova BY 61.46 17 123.37 13 184.83 OLY Female
2022 14 Olga Mikutina AT 61.14 18 121.06 14 182.20 OLY Female
2022 15 Ekaterina Ryabova AZ 61.82 16 118.15 15 179.97 OLY Female
2022 16 Karen Chen US 64.11 13 115.82 17 179.93 OLY Female
2022 17 Nicole Schott DE 63.13 14 114.52 19 177.65 OLY Female
2022 18 Lindsay Van Zundert NL 59.24 22 116.57 16 175.81 OLY Female
2022 19 Madeline Schizas CA 60.53 20 115.03 18 175.56 OLY Female
2022 20 Eliska Brezinova CZ 64.31 12 111.10 21 175.41 OLY Female
2022 21 Eva Lotta Kiibus EE 59.55 21 112.20 20 171.75 OLY Female
2022 22 Alexia Paganini CH 61.06 19 107.85 22 168.91 OLY Female
2022 23 Mana Kawabe JP 62.69 15 104.04 23 166.73 OLY Female
2022 24 Alexandra Feigin BG 59.16 23 100.15 24 159.31 OLY Female
2022 25 Jenni Saarinen FI 56.97 25 96.07 25 153.04 OLY Female
2022 26 Josefin Taljegård SE 54.51 26 NA NA NA OLY Female
2022 27 Yi Zhu CN 53.44 27 NA NA NA OLY Female
2022 28 Natasha Mckay GB 52.54 28 NA NA NA OLY Female
2022 29 Kailani Craine AU 49.93 29 NA NA NA OLY Female
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2019 32 Paulina Ramanauskaite LT 42.31 32 NA NA NA EC Female
2019 15 Paul Fentz DE 69.70 17 140.26 12 209.96 EC Male
2019 27 Nikolaj Majorov SE 59.68 27 NA NA NA EC Male
2019 16 Nicole Schott DE 50.68 19 98.58 16 149.26 EC Female
2019 9 Nicole Rajičová SK 64.08 5 104.95 12 169.03 EC Female
2019 29 Nicky-Leo Obreykov BG 56.54 29 NA NA NA EC Male
2019 21 Nathalie Weinzierl DE 46.09 24 88.49 21 134.58 EC Female
2019 20 Natasha Mckay GB 48.20 22 91.88 19 140.08 EC Female
2019 10 Morisi Kvitelashvili GE 73.04 15 146.75 7 219.79 EC Male
2019 5 Mikhail Kolyada RU 100.49 1 140.38 11 240.87 EC Male
2019 7 Michal Březina CZ 83.66 8 150.59 6 234.25 EC Male
2019 35 Michael Neuman SK 53.38 35 NA NA NA EC Male
2019 14 Maxim Kovtun RU 87.70 5 128.48 16 216.18 EC Male
2019 19 Matyas Belohradsky CZ 67.40 18 123.82 21 191.22 EC Male
2019 3 Matteo Rizzo IT 81.41 10 165.67 3 247.08 EC Male
2019 7 Maé-Bérénice Méité FR 58.95 8 118.15 5 177.10 EC Female
2019 31 Lukas Britschgi CH 55.86 31 NA NA NA EC Male
2019 19 Lucrezia Gennaro IT 52.91 16 90.19 20 143.10 EC Female
2019 20 Luc Maierhofer AT 63.63 21 125.37 19 189.00 EC Male
2019 5 Laurine Lecavelier FR 63.29 6 116.76 6 180.05 EC Female
2019 29 Lara Naki Gutmann IT 43.96 29 NA NA NA EC Female
2019 28 Kyarha Van Tiel NL 44.00 28 NA NA NA EC Female
2019 4 Kévin Aymoz FR 88.02 4 158.32 4 246.34 EC Male
2019 14 Julia Sauter RO 54.29 14 98.86 15 153.15 EC Female
2019 1 Javier Fernández ES 91.84 3 179.75 1 271.59 EC Male
2019 13 Ivett Tóth HU 54.90 13 105.93 10 160.83 EC Female
2019 22 Ivan Shmuratko UA 67.26 19 111.03 24 178.29 EC Male
2019 23 Irakli Maysuradze GE 60.89 24 113.54 22 174.43 EC Male
2019 25 Ihor Reznichenko PL 59.99 25 NA NA NA EC Male
2019 34 Héctor Alonso Serrano ES 53.94 34 NA NA NA EC Male
2019 34 Hana Cvijanović HR 38.00 34 NA NA NA EC Female
2019 21 Graham Newberry GB 61.33 22 127.29 17 188.62 EC Male
2019 27 Gerli Liinamäe EE 44.08 27 NA NA NA EC Female
2019 8 Emmi Peltonen FI 58.06 10 111.97 8 170.03 EC Female
2019 25 Elzbieta Gabryszak PL 45.77 25 NA NA NA EC Female
2019 35 Elizabete Jubkane LV 37.75 35 NA NA NA EC Female
2019 10 Eliska Brezinova CZ 55.85 12 110.92 9 166.77 EC Female
2019 12 Ekaterina Ryabova AZ 59.95 7 103.22 13 163.17 EC Female
2019 11 Deniss Vasiljevs LV 78.87 12 140.63 10 219.50 EC Male
2019 24 Davidé Lewton Brain MC 61.07 23 112.54 23 173.61 EC Male
2019 17 Daša Grm SI 53.50 15 93.79 17 147.29 EC Female
2019 13 Daniel Samohin IL 86.48 6 130.69 14 217.17 EC Male
2019 6 Daniel Grassl IT 81.69 9 155.01 5 236.70 EC Male
2019 33 Conor Stakelum IE 55.03 33 NA NA NA EC Male
2019 33 Camila Gjersem NO 39.81 33 NA NA NA EC Female
2019 28 Burak Demirboga TR 56.95 28 NA NA NA EC Male
2019 24 Antonina Dubinina RS 47.20 23 73.05 24 120.25 EC Female
2019 18 Anita Östlund SE 52.76 17 91.90 18 144.66 EC Female
2019 22 Anastasiya Galustyan AM 48.38 21 84.25 22 132.63 EC Female
2019 36 Anastasia Gozhva UA 35.51 36 NA NA NA EC Female
2019 2 Alina Zagitova RU 75.00 1 123.34 4 198.34 EC Female
2019 6 Alexia Paganini CH 65.64 3 114.26 7 179.90 EC Female
2019 9 Alexei Bychenko IL 84.19 7 136.31 13 220.50 EC Male
2019 11 Alexandra Feigin BG 58.80 9 105.40 11 164.20 EC Female
2019 2 Alexander Samarin RU 91.97 2 177.87 2 269.84 EC Male
2019 8 Alexander Majorov SE 79.88 11 145.50 8 225.38 EC Male
2019 37 Alexander Borovoj HU 46.56 37 NA NA NA EC Male
2019 17 Aleksandr Selevko EE 69.94 16 125.19 20 195.13 EC Male
2019 12 Adam Siao Him Fa FR 76.70 13 141.36 9 218.06 EC Male
2019 11 Yura Matsuda JP 53.35 10 104.24 10 157.59 GPCAN Female
2019 6 Wakaba Higuchi JP 66.51 2 114.78 7 181.29 GPCAN Female
2019 7 Starr Andrews US 64.77 4 109.95 9 174.72 GPCAN Female
2019 1 Shoma Uno JP 88.87 2 188.38 1 277.25 GPCAN Male
2019 12 Roman Sadovsky CA 67.72 12 142.88 8 210.60 GPCAN Male
2019 5 Nam Nguyen CA 82.22 7 158.72 5 240.94 GPCAN Male
2019 4 Mariah Bell US 63.35 5 126.90 4 190.25 GPCAN Female
2019 2 Mako Yamashita JP 66.30 3 136.76 2 203.06 GPCAN Female
2019 7 Kévin Aymoz FR 78.83 10 151.26 7 230.09 GPCAN Male
2019 2 Keegan Messing CA 95.05 1 170.12 2 265.17 GPCAN Male
2019 9 Kazuki Tomono JP 81.63 8 139.20 10 220.83 GPCAN Male
2019 3 Junhwan Cha KR 88.86 3 165.91 3 254.77 GPCAN Male
2019 6 Jason Brown US 76.46 11 158.51 6 234.97 GPCAN Male
2019 3 Evgenia Medvedeva RU 60.83 7 137.08 1 197.91 GPCAN Female
2019 1 Elizaveta Tuktamysheva RU 74.22 1 129.10 3 203.32 GPCAN Female
2019 5 Elizabet Tursynbayeva KZ 61.19 6 124.52 5 185.71 GPCAN Female
2019 9 Daria Panenkova RU 51.41 11 117.13 6 168.54 GPCAN Female
2019 8 Daniel Samohin IL 84.90 5 140.99 9 225.89 GPCAN Male
2019 11 Brendan Kerry AU 80.99 9 139.09 11 220.08 GPCAN Male
2019 10 Alicia Pineault CA 59.02 9 99.27 11 158.29 GPCAN Female
2019 4 Alexander Samarin RU 88.06 4 160.72 4 248.78 GPCAN Male
2019 10 Alexander Majorov SE 84.64 6 135.66 12 220.30 GPCAN Male
2019 8 Alaine Chartrand CA 60.47 8 111.70 8 172.17 GPCAN Female
2019 5 Sofia Samodurova RU 68.24 5 136.09 5 204.33 GPF Female
2019 2 Shoma Uno JP 91.67 2 183.43 2 275.10 GPF Male
2019 6 Sergei Voronov RU 82.96 5 143.48 6 226.44 GPF Male
2019 6 Satoko Miyahara JP 67.52 6 133.79 6 201.31 GPF Female
2019 1 Rika Kihira JP 82.51 1 150.61 1 233.12 GPF Female
2019 1 Nathan Chen US 92.99 1 189.43 1 282.42 GPF Male
2019 4 Michal Březina CZ 89.21 3 166.05 4 255.26 GPF Male
2019 5 Keegan Messing CA 79.56 6 156.49 5 236.05 GPF Male
2019 4 Kaori Sakamoto JP 70.23 4 141.45 4 211.68 GPF Female
2019 3 Junhwan Cha KR 89.07 4 174.42 3 263.49 GPF Male
2019 3 Elizaveta Tuktamysheva RU 70.65 3 144.67 3 215.32 GPF Female
2019 2 Alina Zagitova RU 77.93 2 148.60 2 226.53 GPF Female
2019 1 Yuzuru Hanyu JP 106.69 1 190.43 1 297.12 GPFIN Male
2019 4 Yuna Shiraiwa JP 63.77 2 127.69 5 191.46 GPFIN Female
2019 8 Viveca Lindfors FI 52.95 10 106.67 6 159.62 GPFIN Female
2019 11 Valtter Virtanen FI 48.16 11 106.58 11 154.74 GPFIN Male
2019 2 Stanislava Konstantinova RU 62.56 4 135.01 3 197.57 GPFIN Female
2019 10 Rika Hongo JP 51.11 11 105.48 7 156.59 GPFIN Female
2019 10 Phillip Harris GB 58.99 10 123.67 10 182.66 GPFIN Male
2019 4 Mikhail Kolyada RU 81.76 6 157.03 4 238.79 GPFIN Male
2019 2 Michal Březina CZ 93.31 2 164.67 2 257.98 GPFIN Male
2019 5 Loena Hendrickx BE 63.17 3 128.05 4 191.22 GPFIN Female
2019 8 Keiji Tanaka JP 80.60 7 126.22 9 206.82 GPFIN Male
2019 3 Kaori Sakamoto JP 57.26 7 140.16 2 197.42 GPFIN Female
2019 3 Junhwan Cha KR 82.82 4 160.37 3 243.19 GPFIN Male
2019 7 Hanul Kim KR 55.38 8 104.77 8 160.15 GPFIN Female
2019 9 Emmi Peltonen FI 59.90 5 98.82 10 158.72 GPFIN Female
2019 6 Daria Panenkova RU 58.23 6 103.25 9 161.48 GPFIN Female
2019 5 Boyang Jin CN 85.97 3 141.31 5 227.28 GPFIN Male
2019 11 Angela Wang US 53.76 9 95.81 11 149.57 GPFIN Female
2019 6 Andrei Lazukin RU 82.54 5 135.68 7 218.22 GPFIN Male
2019 1 Alina Zagitova RU 68.90 1 146.39 1 215.29 GPFIN Female
2019 7 Alexei Krasnozhon US 74.05 8 136.98 6 211.03 GPFIN Male
2019 9 Alexei Bychenko IL 73.44 9 128.89 8 202.33 GPFIN Male
2019 6 Romain Ponsart FR 84.97 4 144.89 6 229.86 GPFRA Male
2019 NA Nicolas Nadeau CA 61.46 11 NA NA NA GPFRA Male
2019 1 Nathan Chen US 86.94 3 184.64 1 271.58 GPFRA Male
2019 5 Kévin Aymoz FR 81.00 6 150.16 5 231.16 GPFRA Male
2019 8 Keiji Tanaka JP 79.35 8 136.97 8 216.32 GPFRA Male
2019 2 Jason Brown US 96.41 1 159.92 3 256.33 GPFRA Male
2019 4 Dmitri Aliev RU 75.15 9 162.67 2 237.82 GPFRA Male
2019 7 Deniss Vasiljevs LV 82.30 5 138.96 7 221.26 GPFRA Male
2019 10 Daniel Samohin IL 72.33 10 133.66 9 205.99 GPFRA Male
2019 9 Boyang Jin CN 79.41 7 129.48 10 208.89 GPFRA Male
2019 3 Alexander Samarin RU 90.86 2 156.23 4 247.09 GPFRA Male
2019 12 Yaroslav Paniot UA 68.59 9 105.05 12 173.64 GPJPN Male
2019 4 Vincent Zhou US 75.90 5 147.52 4 223.42 GPJPN Male
2019 6 Sota Yamamoto JP 74.98 6 138.42 5 213.40 GPJPN Male
2019 1 Shoma Uno JP 92.49 1 183.96 1 276.45 GPJPN Male
2019 2 Sergei Voronov RU 91.37 2 162.91 2 254.28 GPJPN Male
2019 2 Satoko Miyahara JP 76.08 2 143.39 2 219.47 GPJPN Female
2019 1 Rika Kihira JP 69.59 5 154.72 1 224.31 GPJPN Female
2019 3 Matteo Rizzo IT 77.00 4 147.71 3 224.71 GPJPN Male
2019 5 Mariah Bell US 62.97 7 135.99 4 198.96 GPJPN Female
2019 9 Maria Sotskova RU 60.75 9 116.24 9 176.99 GPJPN Female
2019 4 Mai Mihara JP 70.38 3 133.82 5 204.20 GPJPN Female
2019 10 Maé-Bérénice Méité FR 50.49 12 112.09 10 162.58 GPJPN Female
2019 11 Kevin Reynolds CA 61.14 12 121.53 10 182.67 GPJPN Male
2019 12 Kailani Craine AU 58.21 11 96.01 12 154.22 GPJPN Female
2019 9 Junehyoung Lee KR 66.16 11 122.10 9 188.26 GPJPN Male
2019 10 Hiroaki Sato JP 67.38 10 117.80 11 185.18 GPJPN Male
2019 6 Eunsoo Lim KR 69.78 4 126.53 6 196.31 GPJPN Female
2019 3 Elizaveta Tuktamysheva RU 76.17 1 142.85 3 219.02 GPJPN Female
2019 5 Dmitri Aliev RU 81.16 3 138.36 6 219.52 GPJPN Male
2019 8 Deniss Vasiljevs LV 72.39 7 125.21 8 197.60 GPJPN Male
2019 8 Courtney Hicks US 59.10 10 118.97 8 178.07 GPJPN Female
2019 11 Angela Wang US 60.82 8 98.54 11 159.36 GPJPN Female
2019 7 Alexander Johnson US 72.03 8 127.72 7 199.75 GPJPN Male
2019 7 Alena Leonova RU 68.22 6 125.93 7 194.15 GPJPN Female
2019 1 Yuzuru Hanyu JP 110.53 1 167.89 1 278.42 GPRUS Male
2019 9 Yura Matsuda JP 52.00 8 85.99 9 137.99 GPRUS Female
2019 5 Yuna Shiraiwa JP 60.35 5 120.58 4 180.93 GPRUS Female
2019 2 Sofia Samodurova RU 67.40 2 130.61 2 198.01 GPRUS Female
2019 8 Polina Tsurskaya RU 56.81 7 92.64 8 149.45 GPRUS Female
2019 6 Paul Fentz DE 78.28 5 142.29 7 220.57 GPRUS Male
2019 2 Morisi Kvitelashvili GE 89.94 2 158.64 2 248.58 GPRUS Male
2019 4 Mikhail Kolyada RU 69.10 8 156.32 4 225.42 GPRUS Male
2019 7 Mako Yamashita JP 51.00 9 110.22 7 161.22 GPRUS Female
2019 5 Keegan Messing CA 73.83 7 146.92 6 220.75 GPRUS Male
2019 3 Kazuki Tomono JP 82.26 4 156.47 3 238.73 GPRUS Male
2019 12 Julian Zhi-Jie Yee MY 60.37 12 118.34 12 178.71 GPRUS Male
2019 NA Gracie Gold US 37.51 10 NA NA NA GPRUS Female
2019 3 Eunsoo Lim KR 57.76 6 127.91 3 185.67 GPRUS Female
2019 6 Elizabet Tursynbayeva KZ 61.73 4 118.72 6 180.45 GPRUS Female
2019 10 Brendan Kerry AU 65.22 10 132.37 9 197.59 GPRUS Male
2019 11 Artur Dmitriev RU 67.58 9 122.00 11 189.58 GPRUS Male
2019 7 Andrei Lazukin RU 62.45 11 153.33 5 215.78 GPRUS Male
2019 1 Alina Zagitova RU 80.78 1 142.17 1 222.95 GPRUS Female
2019 4 Alexia Paganini CH 63.43 3 119.07 5 182.50 GPRUS Female
2019 8 Alexei Krasnozhon US 75.32 6 132.69 8 208.01 GPRUS Male
2019 9 Alexander Majorov SE 82.33 3 123.26 10 205.59 GPRUS Male
2019 5 Vincent Zhou US 76.38 6 149.37 3 225.75 GPUSA Male
2019 10 Starr Andrews US 56.03 9 94.53 10 150.56 GPUSA Female
2019 3 Sofia Samodurova RU 64.41 3 134.29 3 198.70 GPUSA Female
2019 3 Sergei Voronov RU 78.18 4 148.26 4 226.44 GPUSA Male
2019 1 Satoko Miyahara JP 73.86 1 145.85 1 219.71 GPUSA Female
2019 10 Romain Ponsart FR 71.48 8 116.44 11 187.92 GPUSA Male
2019 7 Polina Tsurskaya RU 58.42 8 101.03 8 159.45 GPUSA Female
2019 1 Nathan Chen US 90.58 1 189.99 1 280.57 GPUSA Male
2019 6 Nam Nguyen CA 69.86 9 143.13 6 212.99 GPUSA Male
2019 8 Morisi Kvitelashvili GE 68.58 11 136.54 7 205.12 GPUSA Male
2019 2 Michal Březina CZ 82.09 2 157.42 2 239.51 GPUSA Male
2019 6 Megan Wessenberg US 60.20 6 110.13 6 170.33 GPUSA Female
2019 4 Matteo Rizzo IT 78.09 5 147.72 5 225.81 GPUSA Male
2019 8 Marin Honda JP 62.74 4 95.30 9 158.04 GPUSA Female
2019 NA Loena Hendrickx BE 54.13 10 NA NA NA GPUSA Female
2019 5 Laurine Lecavelier FR 59.57 7 112.84 5 172.41 GPUSA Female
2019 11 Kevin Reynolds CA 61.62 12 124.01 10 185.63 GPUSA Male
2019 2 Kaori Sakamoto JP 71.29 2 142.61 2 213.90 GPUSA Female
2019 7 Julian Zhi-Jie Yee MY 81.52 3 125.99 9 207.51 GPUSA Male
2019 12 Jimmy Ma US 71.53 7 113.53 12 185.06 GPUSA Male
2019 4 Bradie Tennell US 61.72 5 131.17 4 192.89 GPUSA Female
2019 9 Alexei Bychenko IL 69.69 10 127.78 8 197.47 GPUSA Male
2019 9 Alaine Chartrand CA 46.99 11 108.50 7 155.49 GPUSA Female
2019 5 Stanislava Konstantinova RU 54.91 10 134.76 4 189.67 GPFRA Female
2019 1 Rika Kihira JP 67.64 2 138.28 1 205.92 GPFRA Female
2019 12 Matilda Algotsson SE 48.58 12 97.77 11 146.35 GPFRA Female
2019 6 Marin Honda JP 65.37 4 123.24 6 188.61 GPFRA Female
2019 7 Maria Sotskova RU 61.76 5 115.83 7 177.59 GPFRA Female
2019 2 Mai Mihara JP 67.95 1 134.86 3 202.81 GPFRA Female
2019 8 Maé-Bérénice Méité FR 60.86 7 107.16 8 168.02 GPFRA Female
2019 11 Léa Serna FR 55.31 9 94.18 12 149.49 GPFRA Female
2019 9 Laurine Lecavelier FR 51.66 11 105.58 9 157.24 GPFRA Female
2019 4 Evgenia Medvedeva RU 67.55 3 125.26 5 192.81 GPFRA Female
2019 3 Bradie Tennell US 61.34 6 136.44 2 197.78 GPFRA Female
2019 10 Alexia Paganini CH 56.88 8 99.63 10 156.51 GPFRA Female
2018 1 Kaori Sakamoto JP 71.34 2 142.87 1 214.21 4CC Female
2018 2 Mai Mihara JP 69.84 3 140.73 2 210.57 4CC Female
2018 3 Satoko Miyahara JP 71.74 1 135.28 3 207.02 4CC Female
2018 4 Dabin Choi KR 62.30 5 127.93 4 190.23 4CC Female
2018 5 Mariah Bell US 62.90 4 122.94 5 185.84 4CC Female
2018 6 Hanul Kim KR 61.15 6 111.95 8 173.10 4CC Female
2018 7 Starr Andrews US 60.61 7 112.04 7 172.65 4CC Female
2018 8 Alaine Chartrand CA 59.86 8 112.55 6 172.41 4CC Female
2018 9 Angela Wang US 58.97 9 102.07 11 161.04 4CC Female
2018 10 Xiangning Li CN 57.01 10 103.39 10 160.40 4CC Female
2018 11 Soyoun Park KR 53.05 12 106.43 9 159.48 4CC Female
2018 12 Elizabet Tursynbayeva KZ 56.52 11 99.67 13 156.19 4CC Female
2018 13 Alicia Pineault CA 51.53 14 101.28 12 152.81 4CC Female
2018 14 Brooklee Han AU 52.29 13 98.36 14 150.65 4CC Female
2018 15 Michelle Long CA 49.77 17 97.73 15 147.50 4CC Female
2018 16 Kailani Craine AU 50.79 16 90.25 16 141.04 4CC Female
2018 17 Ziquan Zhao CN 48.78 18 90.23 17 139.01 4CC Female
2018 18 Amy Lin TW 51.14 15 86.26 18 137.40 4CC Female
2018 19 Chloe Ing SG 45.30 19 84.40 20 129.70 4CC Female
2018 20 Aiza Imambek KZ 36.47 20 84.53 19 121.00 4CC Female
2018 21 Joanna So HK 35.51 21 72.18 21 107.69 4CC Female
2018 22 Natalie Pailin Sangkagalo TH 33.50 22 60.11 22 93.61 4CC Female
2018 23 Thita Lamsam TH 28.26 23 51.07 23 79.33 4CC Female
2018 1 Boyang Jin CN 100.17 2 200.78 1 300.95 4CC Male
2018 2 Shoma Uno JP 100.49 1 197.45 2 297.94 4CC Male
2018 3 Jason Brown US 89.78 4 179.44 3 269.22 4CC Male
2018 4 Keiji Tanaka JP 90.68 3 169.63 5 260.31 4CC Male
2018 5 Max Aaron US 84.15 6 171.30 4 255.45 4CC Male
2018 6 Misha Ge UZ 82.27 8 166.69 7 248.96 4CC Male
2018 7 Kevin Reynolds CA 74.65 13 166.85 6 241.50 4CC Male
2018 8 Elladj Baldé CA 75.17 12 163.03 8 238.20 4CC Male
2018 9 Nam Nguyen CA 84.09 7 153.43 10 237.52 4CC Male
2018 10 Han Yan CN 84.74 5 143.19 12 227.93 4CC Male
2018 11 Grant Hochstein US 70.80 15 155.59 9 226.39 4CC Male
2018 12 Takahito Mura JP 76.66 10 148.75 11 225.41 4CC Male
2018 13 Brendan Kerry AU 79.57 9 140.38 14 219.95 4CC Male
2018 14 Junehyoung Lee KR 69.93 16 141.93 13 211.86 4CC Male
2018 15 Denis Ten KZ 75.30 11 135.52 15 210.82 4CC Male
2018 16 Julian Zhi-Jie Yee MY 68.45 17 129.23 16 197.68 4CC Male
2018 17 Chih-I Tsao TW 72.57 14 122.64 19 195.21 4CC Male
2018 18 Donovan Carrillo MX 59.07 22 126.84 17 185.91 4CC Male
2018 19 He Zhang CN 63.62 19 121.20 20 184.82 4CC Male
2018 20 Geonhyeong An KR 56.67 23 123.59 18 180.26 4CC Male
2018 21 Andrew Dodds AU 63.69 18 114.12 23 177.81 4CC Male
2018 22 Sihyeong Lee KR 62.65 20 114.42 22 177.07 4CC Male
2018 23 Abzal Rakimgaliev KZ 60.77 21 114.81 21 175.58 4CC Male
2018 24 Leslie Man Cheuk Ip HK 53.80 24 96.43 24 150.23 4CC Male
2018 25 Micah Kai Lynette TH 53.29 25 NA NA NA 4CC Male
2018 26 Harrison Jon Yen Wong HK 52.78 26 NA NA NA 4CC Male
2018 27 Kai Xiang Chew MY 50.92 27 NA NA NA 4CC Male
2018 28 Mark Webster AU 49.45 28 NA NA NA 4CC Male
2018 29 Harry Hau Yin Lee HK 43.98 29 NA NA NA 4CC Male
2018 30 Micah Tang TW 43.05 30 NA NA NA 4CC Male
2018 1 Alina Zagitova RU 80.27 1 157.97 1 238.24 EC Female
2018 2 Evgenia Medvedeva RU 78.57 2 154.29 2 232.86 EC Female
2018 3 Carolina Kostner IT 78.30 3 125.95 4 204.25 EC Female
2018 4 Maria Sotskova RU 68.70 4 132.11 3 200.81 EC Female
2018 5 Loena Hendrickx BE 55.13 8 121.78 5 176.91 EC Female
2018 6 Nicole Rajičová SK 61.01 5 110.89 6 171.90 EC Female
2018 7 Alexia Paganini CH 54.95 9 106.67 9 161.62 EC Female
2018 8 Maé-Bérénice Méité FR 54.14 10 105.56 10 159.70 EC Female
2018 9 Emmi Peltonen FI 52.68 11 106.80 8 159.48 EC Female
2018 10 Nicole Schott DE 48.37 18 109.47 7 157.84 EC Female
2018 11 Laurine Lecavelier FR 55.36 7 98.75 12 154.11 EC Female
2018 12 Eliska Brezinova CZ 52.06 12 97.63 14 149.69 EC Female
2018 13 Ivett Tóth HU 50.70 15 98.28 13 148.98 EC Female
2018 14 Viveca Lindfors FI 51.62 14 96.27 17 147.89 EC Female
2018 15 Micol Cristini IT 48.22 19 99.58 11 147.80 EC Female
2018 16 Lea Johanna Dastich DE 49.89 16 96.93 15 146.82 EC Female
2018 17 Anita Östlund SE 56.04 6 89.10 20 145.14 EC Female
2018 18 Anne Line Gjersem NO 48.70 17 93.98 18 142.68 EC Female
2018 19 Giada Russo IT 45.81 23 96.57 16 142.38 EC Female
2018 20 Daša Grm SI 47.40 20 89.91 19 137.31 EC Female
2018 21 Pernille Sørensen DK 45.76 24 88.18 21 133.94 EC Female
2018 22 Elzbieta Kropa LT 46.06 21 87.81 22 133.87 EC Female
2018 23 Anna Khnychenkova UA 51.84 13 80.86 23 132.70 EC Female
2018 24 Silvia Hugec SK 45.98 22 77.47 24 123.45 EC Female
2018 25 Kristina Lisovskaja EE 45.74 25 NA NA NA EC Female
2018 26 Kyarha Van Tiel NL 45.28 26 NA NA NA EC Female
2018 27 Natasha Mckay GB 45.12 27 NA NA NA EC Female
2018 28 Fruzsina Medgyesi HU 44.71 28 NA NA NA EC Female
2018 29 Julia Sauter RO 44.57 29 NA NA NA EC Female
2018 30 Natalie Klotz AT 43.53 30 NA NA NA EC Female
2018 31 Matilda Algotsson SE 43.28 31 NA NA NA EC Female
2018 32 Sila Saygi TR 43.05 32 NA NA NA EC Female
2018 33 Valentina Matos ES 39.66 33 NA NA NA EC Female
2018 34 Elzbieta Gabryszak PL 38.00 34 NA NA NA EC Female
2018 35 Kim Cheremsky AZ 37.61 35 NA NA NA EC Female
2018 36 Diana Nikitina LV 36.71 36 NA NA NA EC Female
2018 37 Antonina Dubinina RS 36.69 37 NA NA NA EC Female
2018 38 Aimee Buchanan IL 33.87 38 NA NA NA EC Female
2018 39 Presiyana Dimitrova BG 24.76 39 NA NA NA EC Female
2018 1 Javier Fernández ES 103.82 1 191.73 1 295.55 EC Male
2018 2 Dmitri Aliev RU 91.33 2 182.73 2 274.06 EC Male
2018 3 Mikhail Kolyada RU 83.41 4 175.49 3 258.90 EC Male
2018 4 Deniss Vasiljevs LV 85.11 3 158.41 5 243.52 EC Male
2018 5 Alexei Bychenko IL 74.97 8 163.47 4 238.44 EC Male
2018 6 Alexander Samarin RU 74.25 9 155.56 6 229.81 EC Male
2018 7 Alexander Majorov SE 71.28 12 154.58 7 225.86 EC Male
2018 8 Michal Březina CZ 72.72 10 152.48 8 225.20 EC Male
2018 9 Matteo Rizzo IT 78.26 6 141.17 9 219.43 EC Male
2018 10 Jorik Hendrickx BE 78.56 5 139.61 12 218.17 EC Male
2018 11 Chafik Besseghier FR 70.35 13 140.82 10 211.17 EC Male
2018 12 Morisi Kvitelashvili GE 76.74 7 133.73 14 210.47 EC Male
2018 13 Phillip Harris GB 67.77 15 140.45 11 208.22 EC Male
2018 14 Romain Ponsart FR 61.45 20 139.27 13 200.72 EC Male
2018 15 Slavik Hayrapetyan AM 69.49 14 127.14 16 196.63 EC Male
2018 16 Paul Fentz DE 72.54 11 122.43 17 194.97 EC Male
2018 17 Irakli Maysuradze GE 63.69 18 127.53 15 191.22 EC Male
2018 18 Stéphane Walker CH 65.96 16 119.45 20 185.41 EC Male
2018 19 Valtter Virtanen FI 60.23 24 121.54 18 181.77 EC Male
2018 20 Felipe Montoya Pulgarín ES 61.23 22 120.49 19 181.72 EC Male
2018 21 Daniel Albert Naurits EE 60.76 23 115.34 21 176.10 EC Male
2018 22 Sondre Oddvoll Bøe NO 61.85 19 108.79 22 170.64 EC Male
2018 23 Burak Demirboga TR 61.27 21 105.95 23 167.22 EC Male
2018 24 Ihor Reznichenko PL 63.96 17 101.69 24 165.65 EC Male
2018 25 Yaroslav Paniot UA 60.07 25 NA NA NA EC Male
2018 26 Daniel Samohin IL 59.18 26 NA NA NA EC Male
2018 27 Nicholas Vrdoljak HR 58.30 27 NA NA NA EC Male
2018 28 Jiri Belohradsky CZ 58.30 28 NA NA NA EC Male
2018 29 Michael Neuman SK 57.44 29 NA NA NA EC Male
2018 30 Thomas Kennes NL 54.65 30 NA NA NA EC Male
2018 31 Davidé Lewton Brain MC 54.64 31 NA NA NA EC Male
2018 32 Larry Loupolover AZ 52.44 32 NA NA NA EC Male
2018 33 Alexander Maszljanko HU 52.01 33 NA NA NA EC Male
2018 34 Nicky-Leo Obreykov BG 50.24 34 NA NA NA EC Male
2018 35 Yakau Zenko BY 47.26 35 NA NA NA EC Male
2018 36 Conor Stakelum IE 43.05 36 NA NA NA EC Male
2018 1 Kaetlyn Osmond CA 76.06 1 136.85 1 212.91 GPCAN Female
2018 2 Maria Sotskova RU 66.10 3 126.42 2 192.52 GPCAN Female
2018 3 Ashley Wagner US 61.57 7 122.37 4 183.94 GPCAN Female
2018 4 Courtney Hicks US 64.06 4 118.51 5 182.57 GPCAN Female
2018 5 Marin Honda JP 52.60 10 125.64 3 178.24 GPCAN Female
2018 6 Rika Hongo JP 61.60 6 114.74 6 176.34 GPCAN Female
2018 7 Karen Chen US 61.77 5 108.63 7 170.40 GPCAN Female
2018 8 Laurine Lecavelier FR 59.08 8 107.35 8 166.43 GPCAN Female
2018 9 Anna Pogorilaya RU 69.05 2 87.84 10 156.89 GPCAN Female
2018 10 Kailani Craine AU 54.96 9 88.07 9 143.03 GPCAN Female
2018 11 Alaine Chartrand CA 46.51 11 87.66 11 134.17 GPCAN Female
2018 12 Larkyn Austman CA 41.79 12 81.77 12 123.56 GPCAN Female
2018 1 Shoma Uno JP 103.62 1 197.48 1 301.10 GPCAN Male
2018 2 Jason Brown US 90.71 3 170.43 2 261.14 GPCAN Male
2018 3 Alexander Samarin RU 84.02 4 166.04 3 250.06 GPCAN Male
2018 4 Patrick Chan CA 94.43 2 151.27 7 245.70 GPCAN Male
2018 5 Jorik Hendrickx BE 82.08 6 155.23 5 237.31 GPCAN Male
2018 6 Michal Březina CZ 80.34 7 156.70 4 237.04 GPCAN Male
2018 7 Nicolas Nadeau CA 74.23 9 155.20 6 229.43 GPCAN Male
2018 8 Keegan Messing CA 82.17 5 135.58 10 217.75 GPCAN Male
2018 9 Junhwan Cha KR 68.46 11 141.86 8 210.32 GPCAN Male
2018 10 Paul Fentz DE 68.48 10 133.12 11 201.60 GPCAN Male
2018 11 Brendan Kerry AU 63.19 12 138.37 9 201.56 GPCAN Male
2018 12 Takahito Mura JP 74.82 8 111.84 12 186.66 GPCAN Male
2018 1 Alina Zagitova RU 69.44 4 144.44 1 213.88 GPCHN Female
2018 2 Wakaba Higuchi JP 70.53 2 141.99 2 212.52 GPCHN Female
2018 3 Elena Radionova RU 70.48 3 136.34 4 206.82 GPCHN Female
2018 4 Mai Mihara JP 66.90 7 139.17 3 206.07 GPCHN Female
2018 5 Marin Honda JP 66.90 6 131.42 5 198.32 GPCHN Female
2018 6 Gabrielle Daleman CA 70.65 1 126.18 7 196.83 GPCHN Female
2018 7 Elizaveta Tuktamysheva RU 67.10 5 129.58 6 196.68 GPCHN Female
2018 8 Xiangning Li CN 59.20 8 115.62 8 174.82 GPCHN Female
2018 9 Dabin Choi KR 53.90 9 112.09 9 165.99 GPCHN Female
2018 10 Amber Glenn US 52.61 10 98.53 10 151.14 GPCHN Female
2018 11 Ziquan Zhao CN 50.39 11 94.32 11 144.71 GPCHN Female
2018 1 Mikhail Kolyada RU 103.13 1 176.25 3 279.38 GPCHN Male
2018 2 Boyang Jin CN 93.89 2 170.59 5 264.48 GPCHN Male
2018 3 Max Aaron US 83.11 5 176.58 1 259.69 GPCHN Male
2018 4 Vincent Zhou US 80.23 8 176.43 2 256.66 GPCHN Male
2018 5 Han Yan CN 82.22 6 172.39 4 254.61 GPCHN Male
2018 6 Javier Fernández ES 90.57 3 162.49 6 253.06 GPCHN Male
2018 7 Keiji Tanaka JP 87.19 4 159.98 8 247.17 GPCHN Male
2018 8 Kevin Reynolds CA 64.40 10 162.10 7 226.50 GPCHN Male
2018 9 Grant Hochstein US 80.55 7 135.89 9 216.44 GPCHN Male
2018 10 Alexander Majorov SE 64.27 11 121.77 10 186.04 GPCHN Male
2018 11 Alexander Petrov RU 68.58 9 117.44 12 186.02 GPCHN Male
2018 12 He Zhang CN 46.99 12 120.59 11 167.58 GPCHN Male
2018 1 Alina Zagitova RU 76.27 2 147.03 1 223.30 GPF Female
2018 2 Maria Sotskova RU 74.00 4 142.28 2 216.28 GPF Female
2018 3 Kaetlyn Osmond CA 77.04 1 138.12 5 215.16 GPF Female
2018 4 Carolina Kostner IT 72.82 6 141.83 3 214.65 GPF Female
2018 5 Satoko Miyahara JP 74.61 3 138.88 4 213.49 GPF Female
2018 6 Wakaba Higuchi JP 73.26 5 128.85 6 202.11 GPF Female
2018 1 Nathan Chen US 103.32 1 183.19 2 286.51 GPF Male
2018 2 Shoma Uno JP 101.51 2 184.50 1 286.01 GPF Male
2018 3 Mikhail Kolyada RU 99.22 3 182.78 3 282.00 GPF Male
2018 4 Sergei Voronov RU 87.77 5 178.82 4 266.59 GPF Male
2018 5 Adam Rippon US 86.19 6 168.14 5 254.33 GPF Male
2018 6 Jason Brown US 89.02 4 164.79 6 253.81 GPF Male
2018 1 Alina Zagitova RU 62.46 5 151.34 1 213.80 GPFRA Female
2018 2 Maria Sotskova RU 67.79 2 140.99 2 208.78 GPFRA Female
2018 3 Kaetlyn Osmond CA 69.05 1 137.72 4 206.77 GPFRA Female
2018 4 Mai Mihara JP 64.57 4 137.55 5 202.12 GPFRA Female
2018 5 Elizabet Tursynbayeva KZ 62.29 6 138.69 3 200.98 GPFRA Female
2018 6 Yuna Shiraiwa JP 66.05 3 127.13 6 193.18 GPFRA Female
2018 7 Nicole Schott DE 55.54 10 116.85 7 172.39 GPFRA Female
2018 8 Maé-Bérénice Méité FR 58.96 8 112.44 9 171.40 GPFRA Female
2018 9 Elizaveta Tuktamysheva RU 53.03 11 114.62 8 167.65 GPFRA Female
2018 10 Polina Edmunds US 56.31 9 101.46 10 157.77 GPFRA Female
2018 11 Laurine Lecavelier FR 60.68 7 93.67 11 154.35 GPFRA Female
2018 1 Javier Fernández ES 107.86 1 175.85 2 283.71 GPFRA Male
2018 2 Shoma Uno JP 93.92 2 179.40 1 273.32 GPFRA Male
2018 3 Misha Ge UZ 85.41 6 172.93 3 258.34 GPFRA Male
2018 4 Alexander Samarin RU 91.51 3 161.62 4 253.13 GPFRA Male
2018 5 Alexei Bychenko IL 86.79 5 160.65 5 247.44 GPFRA Male
2018 6 Morisi Kvitelashvili GE 86.98 4 153.52 8 240.50 GPFRA Male
2018 7 Max Aaron US 78.64 8 158.56 6 237.20 GPFRA Male
2018 8 Denis Ten KZ 83.70 7 144.87 10 228.57 GPFRA Male
2018 9 Vincent Zhou US 66.12 10 156.09 7 222.21 GPFRA Male
2018 10 Kévin Aymoz FR 70.00 9 150.43 9 220.43 GPFRA Male
2018 11 Romain Ponsart FR 63.81 11 134.31 11 198.12 GPFRA Male
2018 1 Evgenia Medvedeva RU 79.99 1 144.40 1 224.39 GPJPN Female
2018 2 Carolina Kostner IT 74.57 2 137.67 3 212.24 GPJPN Female
2018 3 Polina Tsurskaya RU 70.04 3 140.15 2 210.19 GPJPN Female
2018 4 Mirai Nagasu US 65.17 5 129.29 4 194.46 GPJPN Female
2018 5 Satoko Miyahara JP 65.05 6 126.75 6 191.80 GPJPN Female
2018 6 Alena Leonova RU 63.61 7 127.34 5 190.95 GPJPN Female
2018 7 Rika Hongo JP 65.83 4 122.00 7 187.83 GPJPN Female
2018 8 Yuna Shiraiwa JP 57.34 8 114.60 8 171.94 GPJPN Female
2018 9 Mariah Bell US 57.25 9 108.79 10 166.04 GPJPN Female
2018 10 Nicole Rajičová SK 53.36 10 106.42 11 159.78 GPJPN Female
2018 11 Alaine Chartrand CA 49.60 12 109.76 9 159.36 GPJPN Female
2018 12 Soyoun Park KR 51.54 11 84.25 12 135.79 GPJPN Female
2018 1 Sergei Voronov RU 90.06 1 181.06 1 271.12 GPJPN Male
2018 2 Adam Rippon US 84.95 4 177.04 2 261.99 GPJPN Male
2018 3 Alexei Bychenko IL 85.52 2 166.55 3 252.07 GPJPN Male
2018 4 Jason Brown US 85.36 3 160.59 4 245.95 GPJPN Male
2018 5 Keegan Messing CA 80.13 5 155.67 6 235.80 GPJPN Male
2018 6 Deniss Vasiljevs LV 76.51 8 158.29 5 234.80 GPJPN Male
2018 7 Kazuki Tomono JP 79.88 6 152.05 7 231.93 GPJPN Male
2018 8 Dmitri Aliev RU 77.51 7 145.94 9 223.45 GPJPN Male
2018 9 Michal Březina CZ 76.24 9 144.21 10 220.45 GPJPN Male
2018 10 Nam Nguyen CA 65.82 11 148.69 8 214.51 GPJPN Male
2018 11 Hiroaki Sato JP 75.95 10 123.25 11 199.20 GPJPN Male
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2018 2 Carolina Kostner IT 74.62 2 141.36 2 215.98 GPRUS Female
2018 3 Wakaba Higuchi JP 69.60 3 137.57 3 207.17 GPRUS Female
2018 4 Elena Radionova RU 68.75 5 126.77 4 195.52 GPRUS Female
2018 5 Kaori Sakamoto JP 68.88 4 125.12 5 194.00 GPRUS Female
2018 6 Mariah Bell US 63.85 7 124.71 6 188.56 GPRUS Female
2018 7 Valeria Mikhailova RU 63.38 8 121.71 8 185.09 GPRUS Female
2018 8 Elizabet Tursynbayeva KZ 63.92 6 121.03 9 184.95 GPRUS Female
2018 9 Mirai Nagasu US 56.15 9 122.10 7 178.25 GPRUS Female
2018 10 Nicole Schott DE 55.55 10 113.17 10 168.72 GPRUS Female
2018 11 Maé-Bérénice Méité FR 54.24 11 106.72 12 160.96 GPRUS Female
2018 12 Anastasiya Galustyan AM 48.10 12 106.80 11 154.90 GPRUS Female
2018 1 Nathan Chen US 100.54 1 193.25 2 293.79 GPRUS Male
2018 2 Yuzuru Hanyu JP 94.85 2 195.92 1 290.77 GPRUS Male
2018 3 Mikhail Kolyada RU 85.79 4 185.27 3 271.06 GPRUS Male
2018 4 Misha Ge UZ 85.02 5 170.31 4 255.33 GPRUS Male
2018 5 Morisi Kvitelashvili GE 80.67 8 169.59 5 250.26 GPRUS Male
2018 6 Dmitri Aliev RU 88.77 3 150.84 7 239.61 GPRUS Male
2018 7 Nam Nguyen CA 80.74 7 157.71 6 238.45 GPRUS Male
2018 8 Deniss Vasiljevs LV 82.44 6 145.09 9 227.53 GPRUS Male
2018 9 Denis Ten KZ 69.00 10 145.35 8 214.35 GPRUS Male
2018 10 Andrei Lazukin RU 78.54 9 133.60 11 212.14 GPRUS Male
2018 11 Grant Hochstein US 67.56 11 138.53 10 206.09 GPRUS Male
2018 12 Daniel Samohin IL 62.02 12 121.77 12 183.79 GPRUS Male
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2018 2 Kaori Sakamoto JP 69.40 2 141.19 2 210.59 GPUSA Female
2018 3 Bradie Tennell US 67.01 4 137.09 3 204.10 GPUSA Female
2018 4 Polina Tsurskaya RU 63.20 8 132.36 4 195.56 GPUSA Female
2018 5 Serafima Sakhanovich RU 66.28 5 123.47 5 189.75 GPUSA Female
2018 6 Gabrielle Daleman CA 68.08 3 121.06 8 189.14 GPUSA Female
2018 7 Alena Leonova RU 63.91 7 122.02 7 185.93 GPUSA Female
2018 8 Karen Chen US 59.53 9 123.27 6 182.80 GPUSA Female
2018 9 Nicole Rajičová SK 55.43 10 112.18 9 167.61 GPUSA Female
2018 10 Xiangning Li CN 55.24 11 109.08 10 164.32 GPUSA Female
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2018 2 Adam Rippon US 89.04 2 177.41 1 266.45 GPUSA Male
2018 3 Sergei Voronov RU 87.51 3 169.98 3 257.49 GPUSA Male
2018 4 Boyang Jin CN 77.97 6 168.06 4 246.03 GPUSA Male
2018 5 Han Yan CN 85.97 4 142.36 7 228.33 GPUSA Male
2018 6 Ross Miner US 71.59 8 148.03 5 219.62 GPUSA Male
2018 7 Takahito Mura JP 75.05 7 137.72 8 212.77 GPUSA Male
2018 8 Liam Firus CA 65.17 11 145.66 6 210.83 GPUSA Male
2018 9 Kevin Reynolds CA 69.10 10 134.95 9 204.05 GPUSA Male
2018 10 Roman Sadovsky CA 70.85 9 129.25 10 200.10 GPUSA Male
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2018 NA Maxim Kovtun RU 64.98 12 NA NA NA GPUSA Male
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2018 2 Evgenia Medvedeva RU 81.61 2 156.65 1 238.26 OLY Female
2018 3 Kaetlyn Osmond CA 78.87 3 152.15 3 231.02 OLY Female
2018 4 Satoko Miyahara JP 75.94 4 146.44 4 222.38 OLY Female
2018 5 Carolina Kostner IT 73.15 6 139.29 5 212.44 OLY Female
2018 6 Kaori Sakamoto JP 73.18 5 136.53 6 209.71 OLY Female
2018 7 Dabin Choi KR 67.77 8 131.49 8 199.26 OLY Female
2018 8 Maria Sotskova RU 63.86 12 134.24 7 198.10 OLY Female
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2018 10 Mirai Nagasu US 66.93 9 119.61 12 186.54 OLY Female
2018 11 Karen Chen US 65.90 10 119.75 11 185.65 OLY Female
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2018 13 Hanul Kim KR 54.33 21 121.38 10 175.71 OLY Female
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2018 27 Giada Russo IT 50.88 27 NA NA NA OLY Female
2018 28 Anita Östlund SE 49.14 28 NA NA NA OLY Female
2018 29 Anna Khnychenkova UA 47.59 29 NA NA NA OLY Female
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2018 13 Daniel Samohin IL 80.69 18 170.75 11 251.44 OLY Male
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2018 15 Junhwan Cha KR 83.43 15 165.16 14 248.59 OLY Male
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2018 17 Misha Ge UZ 83.90 14 161.04 17 244.94 OLY Male
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2018 28 Michael Christian Martinez PH 55.56 28 NA NA NA OLY Male
2018 29 Felipe Montoya Pulgarín ES 52.41 29 NA NA NA OLY Male
2018 30 Yaroslav Paniot UA 46.58 30 NA NA NA OLY Male
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2017 6 Mariah Bell US 61.21 7 115.89 7 177.10 4CC Female
2017 7 Zijun Li CN 60.37 8 116.68 5 177.05 4CC Female
2017 8 Elizabet Tursynbayeva KZ 66.87 3 109.78 11 176.65 4CC Female
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2017 10 Rika Hongo JP 59.16 9 108.26 13 167.42 4CC Female
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2017 12 Karen Chen US 55.60 12 111.22 10 166.82 4CC Female
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2017 24 Micah Tang TW 46.41 24 89.38 24 135.79 4CC Male
2017 25 Harry Hau Yin Lee HK 45.27 25 NA NA NA 4CC Male
2017 26 Harrison Jon Yen Wong HK 45.12 26 NA NA NA 4CC Male
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2017 4 Maria Sotskova RU 72.17 4 120.35 5 192.52 EC Female
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2017 8 Ivett Tóth HU 61.49 6 111.16 8 172.65 EC Female
2017 9 Roberta Rodeghiero IT 57.77 8 103.23 12 161.00 EC Female
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2017 13 Matilda Algotsson SE 51.35 18 103.28 11 154.63 EC Female
2017 14 Joshi Helgesson SE 53.93 13 98.93 13 152.86 EC Female
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2017 30 Colette Kaminski PL 39.83 30 NA NA NA EC Female
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2017 30 Mark Gorodnitsky IL 51.72 30 NA NA NA EC Male
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2017 33 Daniel Samohin IL 50.33 33 NA NA NA EC Male
2017 34 Michael Neuman SK 47.67 34 NA NA NA EC Male
2017 35 Nicky-Leo Obreykov BG 44.83 35 NA NA NA EC Male
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2017 9 Liam Firus CA 70.09 10 140.80 10 210.89 GPCAN Male
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2017 11 Grant Hochstein US 60.20 12 144.49 8 204.69 GPCAN Male
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2017 2 Kaetlyn Osmond CA 72.20 1 123.80 3 196.00 GPCHN Female
2017 3 Elizaveta Tuktamysheva RU 64.88 4 127.69 2 192.57 GPCHN Female
2017 4 Mai Mihara JP 68.48 3 122.44 4 190.92 GPCHN Female
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2017 6 Ashley Wagner US 64.36 5 117.02 7 181.38 GPCHN Female
2017 7 Karen Chen US 58.28 9 121.11 5 179.39 GPCHN Female
2017 8 Zijun Li CN 61.32 7 111.08 8 172.40 GPCHN Female
2017 9 Courtney Hicks US 59.86 8 103.78 9 163.64 GPCHN Female
2017 10 Xiangning Li CN 54.55 11 102.72 10 157.27 GPCHN Female
2017 11 Ziquan Zhao CN 58.20 10 90.92 12 149.12 GPCHN Female
2017 12 Joshi Helgesson SE 49.25 12 95.39 11 144.64 GPCHN Female
2017 1 Patrick Chan CA 83.41 3 196.31 1 279.72 GPCHN Male
2017 2 Boyang Jin CN 96.17 1 182.37 2 278.54 GPCHN Male
2017 3 Sergei Voronov RU 82.93 4 160.83 4 243.76 GPCHN Male
2017 4 Max Aaron US 81.67 5 161.07 3 242.74 GPCHN Male
2017 5 Han Yan CN 75.04 8 155.15 5 230.19 GPCHN Male
2017 6 Alexander Petrov RU 74.21 9 154.23 6 228.44 GPCHN Male
2017 7 Maxim Kovtun RU 70.10 10 151.33 7 221.43 GPCHN Male
2017 8 Daniel Samohin IL 83.47 2 130.04 10 213.51 GPCHN Male
2017 9 Ross Miner US 76.73 6 136.61 8 213.34 GPCHN Male
2017 10 Michal Březina CZ 75.86 7 135.91 9 211.77 GPCHN Male
2017 1 Evgenia Medvedeva RU 79.21 1 148.45 1 227.66 GPF Female
2017 2 Satoko Miyahara JP 74.64 3 143.69 2 218.33 GPF Female
2017 3 Anna Pogorilaya RU 73.29 4 143.18 3 216.47 GPF Female
2017 4 Kaetlyn Osmond CA 75.54 2 136.91 4 212.45 GPF Female
2017 5 Maria Sotskova RU 65.74 6 133.05 5 198.79 GPF Female
2017 6 Elena Radionova RU 68.98 5 119.83 6 188.81 GPF Female
2017 1 Yuzuru Hanyu JP 106.53 1 187.37 3 293.90 GPF Male
2017 2 Nathan Chen US 85.30 5 197.55 1 282.85 GPF Male
2017 3 Shoma Uno JP 86.82 4 195.69 2 282.51 GPF Male
2017 4 Javier Fernández ES 91.76 3 177.01 4 268.77 GPF Male
2017 5 Patrick Chan CA 99.76 2 166.99 5 266.75 GPF Male
2017 6 Adam Rippon US 83.93 6 149.17 6 233.10 GPF Male
2017 1 Evgenia Medvedeva RU 78.52 1 143.02 1 221.54 GPFRA Female
2017 2 Maria Sotskova RU 68.71 3 131.64 2 200.35 GPFRA Female
2017 3 Wakaba Higuchi JP 65.02 5 129.46 3 194.48 GPFRA Female
2017 4 Gabrielle Daleman CA 72.70 2 119.40 6 192.10 GPFRA Female
2017 5 Soyoun Park KR 64.89 6 120.30 4 185.19 GPFRA Female
2017 6 Laurine Lecavelier FR 66.61 4 118.04 7 184.65 GPFRA Female
2017 7 Maé-Bérénice Méité FR 52.78 11 119.87 5 172.65 GPFRA Female
2017 8 Gracie Gold US 54.87 10 111.02 8 165.89 GPFRA Female
2017 9 Mao Asada JP 61.29 8 100.10 10 161.39 GPFRA Female
2017 10 Yuka Nagai JP 52.41 12 107.08 9 159.49 GPFRA Female
2017 11 Anastasiya Galustyan AM 56.92 9 98.57 11 155.49 GPFRA Female
2017 12 Alena Leonova RU 63.87 7 77.49 12 141.36 GPFRA Female
2017 1 Javier Fernández ES 96.57 1 188.81 1 285.38 GPFRA Male
2017 2 Denis Ten KZ 89.21 3 180.05 3 269.26 GPFRA Male
2017 3 Adam Rippon US 85.25 4 182.28 2 267.53 GPFRA Male
2017 4 Nathan Chen US 92.85 2 171.95 4 264.80 GPFRA Male
2017 5 Takahito Mura JP 78.38 6 170.04 5 248.42 GPFRA Male
2017 6 Jorik Hendrickx BE 80.34 5 150.13 8 230.47 GPFRA Male
2017 7 Misha Ge UZ 72.49 8 156.57 6 229.06 GPFRA Male
2017 8 Chafik Besseghier FR 77.00 7 148.02 9 225.02 GPFRA Male
2017 9 Artur Dmitriev RU 64.48 11 154.22 7 218.70 GPFRA Male
2017 10 Brendan Kerry AU 70.67 9 128.73 10 199.40 GPFRA Male
2017 11 Ivan Righini IT 68.42 10 117.39 11 185.81 GPFRA Male
2017 1 Anna Pogorilaya RU 71.56 1 139.30 1 210.86 GPJPN Female
2017 2 Satoko Miyahara JP 64.20 3 133.80 2 198.00 GPJPN Female
2017 3 Maria Sotskova RU 69.96 2 125.92 3 195.88 GPJPN Female
2017 4 Wakaba Higuchi JP 62.58 5 122.81 4 185.39 GPJPN Female
2017 5 Mirai Nagasu US 63.49 4 116.84 8 180.33 GPJPN Female
2017 6 Karen Chen US 58.76 7 119.69 5 178.45 GPJPN Female
2017 7 Yura Matsuda JP 60.98 6 117.28 7 178.26 GPJPN Female
2017 8 Elizabet Tursynbayeva KZ 55.66 9 119.45 6 175.11 GPJPN Female
2017 9 Dabin Choi KR 51.06 11 114.57 9 165.63 GPJPN Female
2017 10 Alaine Chartrand CA 58.72 8 101.50 11 160.22 GPJPN Female
2017 11 Nicole Rajičová SK 53.43 10 106.27 10 159.70 GPJPN Female
2017 1 Yuzuru Hanyu JP 103.89 1 197.58 1 301.47 GPJPN Male
2017 2 Nathan Chen US 87.94 2 180.97 2 268.91 GPJPN Male
2017 3 Keiji Tanaka JP 80.49 3 167.95 3 248.44 GPJPN Male
2017 4 Alexei Bychenko IL 75.13 7 154.74 4 229.87 GPJPN Male
2017 5 Mikhail Kolyada RU 78.18 4 147.51 6 225.69 GPJPN Male
2017 6 Deniss Vasiljevs LV 70.50 10 153.23 5 223.73 GPJPN Male
2017 7 Jason Brown US 74.33 8 144.14 7 218.47 GPJPN Male
2017 8 Nam Nguyen CA 75.33 6 137.10 8 212.43 GPJPN Male
2017 9 Ryuju Hino JP 72.50 9 134.65 9 207.15 GPJPN Male
2017 10 Elladj Baldé CA 76.29 5 119.03 11 195.32 GPJPN Male
2017 11 Grant Hochstein US 68.31 11 123.09 10 191.40 GPJPN Male
2017 1 Anna Pogorilaya RU 73.93 1 141.28 1 215.21 GPRUS Female
2017 2 Elena Radionova RU 71.93 2 123.67 2 195.60 GPRUS Female
2017 3 Courtney Hicks US 63.68 6 119.30 3 182.98 GPRUS Female
2017 4 Zijun Li CN 63.89 5 117.94 4 181.83 GPRUS Female
2017 5 Elizabet Tursynbayeva KZ 64.31 4 117.01 5 181.32 GPRUS Female
2017 6 Yura Matsuda JP 61.57 7 116.08 6 177.65 GPRUS Female
2017 7 Nicole Rajičová SK 57.91 8 109.65 7 167.56 GPRUS Female
2017 8 Roberta Rodeghiero IT 52.57 12 107.23 8 159.80 GPRUS Female
2017 9 Anastasiya Galustyan AM 55.93 9 103.33 9 159.26 GPRUS Female
2017 10 Angelina Kuchvalska LV 54.29 11 96.80 10 151.09 GPRUS Female
2017 11 Kanako Murakami JP 55.25 10 95.78 11 151.03 GPRUS Female
2017 12 Julia Lipnitskaia RU 69.25 3 78.88 12 148.13 GPRUS Female
2017 1 Javier Fernández ES 91.55 2 201.43 1 292.98 GPRUS Male
2017 2 Shoma Uno JP 98.59 1 186.48 2 285.07 GPRUS Male
2017 3 Alexei Bychenko IL 86.81 4 168.71 3 255.52 GPRUS Male
2017 4 Mikhail Kolyada RU 90.28 3 155.02 6 245.30 GPRUS Male
2017 5 Max Aaron US 73.64 8 161.94 4 235.58 GPRUS Male
2017 6 Elladj Baldé CA 76.36 6 149.09 8 225.45 GPRUS Male
2017 7 Keiji Tanaka JP 69.13 10 155.78 5 224.91 GPRUS Male
2017 8 Chafik Besseghier FR 80.68 5 143.30 10 223.98 GPRUS Male
2017 9 Gordey Gorshkov RU 73.37 9 150.14 7 223.51 GPRUS Male
2017 10 Artur Dmitriev RU 76.06 7 145.46 9 221.52 GPRUS Male
2017 11 Deniss Vasiljevs LV 62.40 12 141.37 11 203.77 GPRUS Male
2017 12 Alexander Majorov SE 67.80 11 124.34 12 192.14 GPRUS Male
2017 1 Ashley Wagner US 69.50 1 126.94 2 196.44 GPUSA Female
2017 2 Mariah Bell US 60.92 6 130.67 1 191.59 GPUSA Female
2017 3 Mai Mihara JP 65.75 2 123.53 3 189.28 GPUSA Female
2017 4 Gabrielle Daleman CA 64.49 4 122.14 4 186.63 GPUSA Female
2017 5 Gracie Gold US 64.87 3 119.35 5 184.22 GPUSA Female
2017 6 Mao Asada JP 64.47 5 112.31 6 176.78 GPUSA Female
2017 7 Serafima Sakhanovich RU 56.52 8 107.32 7 163.84 GPUSA Female
2017 8 Soyoun Park KR 58.16 7 103.20 8 161.36 GPUSA Female
2017 9 Roberta Rodeghiero IT 52.62 9 96.51 10 149.13 GPUSA Female
2017 10 Kanako Murakami JP 47.87 10 97.16 9 145.03 GPUSA Female
2017 11 Angelina Kuchvalska LV 47.80 11 87.17 11 134.97 GPUSA Female
2017 1 Shoma Uno JP 89.15 1 190.19 1 279.34 GPUSA Male
2017 2 Jason Brown US 85.75 3 182.63 2 268.38 GPUSA Male
2017 3 Adam Rippon US 87.32 2 174.11 3 261.43 GPUSA Male
2017 4 Sergei Voronov RU 78.68 5 166.60 5 245.28 GPUSA Male
2017 5 Boyang Jin CN 72.93 8 172.15 4 245.08 GPUSA Male
2017 6 Nam Nguyen CA 79.62 4 159.64 7 239.26 GPUSA Male
2017 7 Maxim Kovtun RU 67.43 10 163.32 6 230.75 GPUSA Male
2017 8 Timothy Dolensky US 77.59 6 148.94 8 226.53 GPUSA Male
2017 9 Jorik Hendrickx BE 76.62 7 148.29 9 224.91 GPUSA Male
2017 10 Brendan Kerry AU 71.62 9 140.14 10 211.76 GPUSA Male

I also merged all the score data from the 5 specified World Championships, adding a year variable for the year the Championships took place.

#merge worlds data
worldmerged <- dplyr::bind_rows(list(world23, world22, world19, world18, world17), .id = 'year') 

#clean names
worldmerged$Skater <- gsub('[0-9]', '', worldmerged$Skater)
worldmerged$Skater <- str_squish(worldmerged$Skater)
colnames(worldmerged) <- c('year', 'worldrank', 'skater', 'nation', 'sp_score', 'sp_rank', 'fs_score', 'fs_rank', 'worldscore')

#add year variable
worldmerged <- worldmerged %>% 
  mutate(year = ifelse(year == 1, 2023,
                ifelse(year == 2, 2022,
                ifelse(year == 3, 2019, 
                ifelse(year == 4, 2018, 2017)))))

worldmerged %>%  kable() %>% 
  kable_styling(full_width = F) %>% 
  scroll_box(width = "100%", height = "200px")
year worldrank skater nation sp_score sp_rank fs_score fs_rank worldscore
2023 1 Shoma Uno 🇯🇵 104.63 1 196.51 1 301.14
2023 2 Junhwan Cha 🇰🇷 99.64 3 196.39 2 296.03
2023 3 Ilia Malinin 🇺🇸 100.38 2 188.06 3 288.44
2023 4 Kévin Aymoz 🇫🇷 95.56 5 187.41 4 282.97
2023 5 Jason Brown 🇺🇸 94.17 6 185.87 5 280.04
2023 6 Kazuki Tomono 🇯🇵 92.68 7 180.73 6 273.41
2023 7 Keegan Messing 🇨🇦 98.75 4 166.41 11 265.16
2023 8 Lukas Britschgi 🇨🇭 86.18 9 171.16 9 257.34
2023 9 Matteo Rizzo 🇮🇹 79.28 13 176.76 7 256.04
2023 10 Adam Siao Him Fa 🇫🇷 79.78 12 173.33 8 253.11
2023 11 Vladimir Litvintsev 🇦🇿 82.71 10 169.05 10 251.76
2023 12 Daniel Grassl 🇮🇹 86.50 8 157.93 14 244.43
2023 13 Deniss Vasiljevs 🇱🇻 82.37 11 160.78 13 243.15
2023 14 Mikhail Shaidorov 🇰🇿 75.41 18 161.52 12 236.93
2023 15 Sota Yamamoto 🇯🇵 75.48 17 156.91 15 232.39
2023 16 Mark Gorodnitsky 🇮🇱 77.89 14 154.24 16 232.13
2023 17 Mihhail Selevko 🇪🇪 76.81 15 154.13 17 230.94
2023 18 Andreas Nordebäck 🇸🇪 73.45 20 150.07 18 223.52
2023 19 Nikita Starostin 🇩🇪 75.53 16 142.34 19 217.87
2023 20 Morisi Kvitelashvili 🇬🇪 73.05 21 139.27 20 212.32
2023 21 Andrew Torgashev 🇺🇸 71.41 22 139.18 21 210.59
2023 22 Boyang Jin 🇨🇳 75.04 19 129.18 23 204.22
2023 23 Adam Hagara 🇸🇰 70.29 24 132.97 22 203.26
2023 24 Maurizio Zandrón 🇦🇹 70.36 23 123.95 24 194.31
2023 25 Kyrylo Marsak 🇺🇦 68.60 25 NA NA NA
2023 26 Conrad Orzel 🇨🇦 67.65 26 NA NA NA
2023 27 Tomás Guarino Sabaté 🇪🇸 67.60 27 NA NA NA
2023 28 Burak Demirboga 🇹🇷 65.73 28 NA NA NA
2023 29 Nika Egadze 🇬🇪 65.17 29 NA NA NA
2023 30 Alexander Zlatkov 🇧🇬 62.31 30 NA NA NA
2023 31 Jari Kessler 🇭🇷 61.94 31 NA NA NA
2023 32 Graham Newberry 🇬🇧 61.70 32 NA NA NA
2023 33 Vladimir Samoilov 🇵🇱 61.48 33 NA NA NA
2023 34 Georgiy Reshtenko 🇨🇿 59.93 34 NA NA NA
2023 1 Kaori Sakamoto 🇯🇵 79.24 1 145.37 2 224.61
2023 2 Haein Lee 🇰🇷 73.62 2 147.32 1 220.94
2023 3 Loena Hendrickx 🇧🇪 71.94 5 138.48 4 210.42
2023 4 Isabeau Levito 🇺🇸 73.03 4 134.62 5 207.65
2023 5 Mai Mihara 🇯🇵 73.46 3 132.24 6 205.70
2023 6 Chaeyeon Kim 🇰🇷 64.06 12 139.45 3 203.51
2023 7 Nicole Schott 🇩🇪 67.29 7 130.47 9 197.76
2023 8 Kimmy Repond 🇨🇭 62.75 13 131.34 8 194.09
2023 9 Niina Petrõkina 🇪🇪 68.00 6 125.49 12 193.49
2023 10 Rinka Watanabe 🇯🇵 60.90 15 131.91 7 192.81
2023 11 Nina Pinzarrone 🇧🇪 62.04 14 129.74 10 191.78
2023 12 Amber Glenn 🇺🇸 65.52 10 122.81 14 188.33
2023 13 Madeline Schizas 🇨🇦 60.02 16 127.47 11 187.49
2023 14 Anastasiia Gubanova 🇬🇪 65.40 11 119.52 15 184.92
2023 15 Bradie Tennell 🇺🇸 66.45 8 117.69 16 184.14
2023 16 Ekaterina Kurakova 🇵🇱 65.69 9 115.74 17 181.43
2023 17 Lara Naki Gutmann 🇮🇹 55.22 23 123.21 13 178.43
2023 18 Yelim Kim 🇰🇷 60.02 17 114.28 19 174.30
2023 19 Olga Mikutina 🇦🇹 57.05 20 115.26 18 172.31
2023 20 Julia Sauter 🇷🇴 56.02 22 109.60 20 165.62
2023 21 Janna Jyrkinen 🇫🇮 56.06 21 104.85 21 160.91
2023 22 Lindsay Van Zundert 🇳🇱 57.56 19 101.99 22 159.55
2023 23 Sofja Stepchenko 🇱🇻 58.87 18 99.51 24 158.38
2023 24 Alexandra Feigin 🇧🇬 54.65 24 101.09 23 155.74
2023 25 Lorine Schild 🇫🇷 54.35 25 NA NA NA
2023 26 Jade Hovine 🇧🇪 54.10 26 NA NA NA
2023 27 Kristen Spours 🇬🇧 53.38 27 NA NA NA
2023 28 Ema Doboszova 🇸🇰 53.01 28 NA NA NA
2023 29 Kristina Isaev 🇩🇪 52.93 29 NA NA NA
2023 30 Anastasia Gracheva 🇲🇩 50.55 30 NA NA NA
2023 31 Marilena Kitromilis 🇨🇾 48.92 31 NA NA NA
2023 32 Eliska Brezinova 🇨🇿 47.29 32 NA NA NA
2023 33 Daša Grm 🇸🇮 47.04 33 NA NA NA
2023 34 Júlia Láng 🇭🇺 44.26 34 NA NA NA
2023 35 Mia Caroline Risa Gomez 🇳🇴 43.54 35 NA NA NA
2022 1 Shoma Uno 🇯🇵 109.63 1 202.85 1 312.48
2022 2 Yuma Kagiyama 🇯🇵 105.69 2 191.91 2 297.60
2022 3 Vincent Zhou 🇺🇸 95.84 6 181.54 4 277.38
2022 4 Morisi Kvitelashvili 🇬🇪 92.61 7 179.42 5 272.03
2022 5 Camden Pulkinen 🇺🇸 89.50 12 182.19 3 271.69
2022 6 Kazuki Tomono 🇯🇵 101.12 3 168.25 8 269.37
2022 7 Daniel Grassl 🇮🇹 97.62 5 169.04 7 266.66
2022 8 Adam Siao Him Fa 🇫🇷 90.97 10 175.15 6 266.12
2022 9 Ilia Malinin 🇺🇸 100.16 4 163.63 11 263.79
2022 10 Matteo Rizzo 🇮🇹 91.67 8 164.08 10 255.75
2022 11 Kévin Aymoz 🇫🇷 85.26 15 160.20 12 245.46
2022 12 Roman Sadovsky 🇨🇦 80.54 18 164.82 9 245.36
2022 13 Deniss Vasiljevs 🇱🇻 90.95 11 152.05 14 243.00
2022 14 Keegan Messing 🇨🇦 91.18 9 143.85 17 235.03
2022 15 Mihhail Selevko 🇪🇪 78.85 20 155.87 13 234.72
2022 16 Vladimir Litvintsev 🇦🇿 85.83 14 147.79 15 233.62
2022 17 Maurizio Zandrón 🇦🇹 83.10 16 145.17 16 228.27
2022 18 Sihyeong Lee 🇰🇷 86.35 13 138.71 18 225.06
2022 19 Nikolaj Majorov 🇸🇪 79.36 19 137.09 20 216.45
2022 20 Graham Newberry 🇬🇧 74.92 21 135.48 21 210.40
2022 21 Tomás Guarino Sabaté 🇪🇸 71.42 24 137.53 19 208.95
2022 22 Nikita Starostin 🇩🇪 73.79 23 131.93 22 205.72
2022 23 Ivan Shmuratko 🇺🇦 73.99 22 122.66 23 196.65
2022 NA Junhwan Cha 🇰🇷 82.43 17 NA NA NA
2022 25 Mark Gorodnitsky 🇮🇱 69.70 25 NA NA NA
2022 26 Adam Hagara 🇸🇰 60.92 26 NA NA NA
2022 27 Vladimir Samoilov 🇵🇱 60.71 27 NA NA NA
2022 28 Burak Demirboga 🇹🇷 52.86 28 NA NA NA
2022 29 Aleksandr Vlasenko 🇭🇺 51.10 29 NA NA NA
2022 1 Kaori Sakamoto 🇯🇵 80.32 1 155.77 1 236.09
2022 2 Loena Hendrickx 🇧🇪 75.00 2 142.70 2 217.70
2022 3 Alysa Liu 🇺🇸 71.91 5 139.28 3 211.19
2022 4 Mariah Bell 🇺🇸 72.55 3 136.11 4 208.66
2022 5 Young You 🇰🇷 72.08 4 132.83 6 204.91
2022 6 Anastasiia Gubanova 🇬🇪 62.59 14 134.02 5 196.61
2022 7 Haein Lee 🇰🇷 64.16 11 132.39 7 196.55
2022 8 Karen Chen 🇺🇸 66.16 8 126.35 8 192.51
2022 9 Ekaterina Ryabova 🇦🇿 65.52 9 122.98 11 188.50
2022 10 Nicole Schott 🇩🇪 67.77 6 120.65 14 188.42
2022 11 Wakaba Higuchi 🇯🇵 67.03 7 121.12 12 188.15
2022 12 Madeline Schizas 🇨🇦 64.20 10 123.94 10 188.14
2022 13 Ekaterina Kurakova 🇵🇱 61.92 16 124.51 9 186.43
2022 14 Olga Mikutina 🇦🇹 62.14 15 120.84 13 182.98
2022 15 Mana Kawabe 🇯🇵 63.68 12 118.76 15 182.44
2022 16 Niina Petrõkina 🇪🇪 60.24 17 116.36 16 176.60
2022 17 Lindsay Van Zundert 🇳🇱 58.49 18 112.90 17 171.39
2022 18 Julia Sauter 🇷🇴 58.07 19 112.24 18 170.31
2022 19 Alexia Paganini 🇨🇭 63.09 13 106.93 19 170.02
2022 20 Lara Naki Gutmann 🇮🇹 57.92 20 106.47 20 164.39
2022 21 Josefin Taljegård 🇸🇪 57.52 21 105.72 21 163.24
2022 22 Kailani Craine 🇦🇺 56.64 22 105.11 22 161.75
2022 23 Natasha Mckay 🇬🇧 55.71 24 103.56 23 159.27
2022 24 Daša Grm 🇸🇮 55.82 23 91.30 24 147.12
2022 25 Jenni Saarinen 🇫🇮 55.30 25 NA NA NA
2022 26 Tzu-Han Ting 🇹🇼 55.24 26 NA NA NA
2022 27 Eliska Brezinova 🇨🇿 55.07 27 NA NA NA
2022 28 Alexandra Feigin 🇧🇬 55.01 28 NA NA NA
2022 29 Léa Serna 🇫🇷 54.30 29 NA NA NA
2022 30 Marilena Kitromilis 🇨🇾 53.32 30 NA NA NA
2022 31 Júlia Láng 🇭🇺 47.93 31 NA NA NA
2022 32 Stefanie Pesendorfer 🇦🇹 47.23 32 NA NA NA
2022 33 Anete Lace 🇱🇻 44.60 33 NA NA NA
2019 1 Nathan Chen 🇺🇸 107.40 1 216.02 1 323.42
2019 2 Yuzuru Hanyu 🇯🇵 94.87 3 206.10 2 300.97
2019 3 Vincent Zhou 🇺🇸 94.17 4 186.99 3 281.16
2019 4 Shoma Uno 🇯🇵 91.40 6 178.92 4 270.32
2019 5 Boyang Jin 🇨🇳 84.26 9 178.45 5 262.71
2019 6 Mikhail Kolyada 🇷🇺 84.23 10 178.21 6 262.44
2019 7 Matteo Rizzo 🇮🇹 93.37 5 164.29 10 257.66
2019 8 Michal Březina 🇨🇿 86.96 8 167.32 8 254.28
2019 9 Jason Brown 🇺🇸 96.81 2 157.34 14 254.15
2019 10 Andrei Lazukin 🇷🇺 84.05 11 164.69 9 248.74
2019 11 Kévin Aymoz 🇫🇷 88.24 7 159.23 12 247.47
2019 12 Alexander Samarin 🇷🇺 78.38 20 167.95 7 246.33
2019 13 Morisi Kvitelashvili 🇬🇪 82.67 12 158.07 13 240.74
2019 14 Keiji Tanaka 🇯🇵 78.76 19 159.64 11 238.40
2019 15 Keegan Messing 🇨🇦 82.38 14 155.26 15 237.64
2019 16 Nam Nguyen 🇨🇦 82.51 13 154.76 16 237.27
2019 17 Vladimir Litvintsev 🇦🇿 81.46 16 149.38 19 230.84
2019 18 Alexander Majorov 🇸🇪 79.17 17 150.55 17 229.72
2019 19 Junhwan Cha 🇰🇷 79.17 18 150.09 18 229.26
2019 20 Brendan Kerry 🇦🇺 78.26 21 143.76 21 222.02
2019 21 Deniss Vasiljevs 🇱🇻 74.74 23 143.78 20 218.52
2019 22 Alexei Bychenko 🇮🇱 77.67 22 138.93 22 216.60
2019 23 Julian Zhi-Jie Yee 🇲🇾 73.63 24 132.34 23 205.97
2019 24 Daniel Samohin 🇮🇱 82.00 15 123.28 24 205.28
2019 25 Peter James Hallam 🇬🇧 66.06 25 NA NA NA
2019 26 Luc Maierhofer 🇦🇹 65.78 26 NA NA NA
2019 27 Aleksandr Selevko 🇪🇪 63.25 27 NA NA NA
2019 28 Paul Fentz 🇩🇪 63.24 28 NA NA NA
2019 29 Ivan Shmuratko 🇺🇦 62.99 29 NA NA NA
2019 30 Burak Demirboga 🇹🇷 60.79 30 NA NA NA
2019 31 Slavik Hayrapetyan 🇦🇲 60.66 31 NA NA NA
2019 32 Valtter Virtanen 🇫🇮 55.73 32 NA NA NA
2019 33 Donovan Carrillo 🇲🇽 54.99 33 NA NA NA
2019 34 Lukas Britschgi 🇨🇭 54.58 34 NA NA NA
2019 35 Ihor Reznichenko 🇵🇱 50.15 35 NA NA NA
2019 1 Alina Zagitova 🇷🇺 82.08 1 155.42 1 237.50
2019 2 Elizabet Tursynbayeva 🇰🇿 75.96 3 148.80 4 224.76
2019 3 Evgenia Medvedeva 🇷🇺 74.23 4 149.57 3 223.80
2019 4 Rika Kihira 🇯🇵 70.90 7 152.59 2 223.49
2019 5 Kaori Sakamoto 🇯🇵 76.86 2 145.97 5 222.83
2019 6 Satoko Miyahara 🇯🇵 70.60 8 145.35 6 215.95
2019 7 Bradie Tennell 🇺🇸 69.50 10 143.97 7 213.47
2019 8 Sofia Samodurova 🇷🇺 70.42 9 138.16 8 208.58
2019 9 Mariah Bell 🇺🇸 71.26 6 136.81 9 208.07
2019 10 Eunsoo Lim 🇰🇷 72.91 5 132.66 10 205.57
2019 11 Gabrielle Daleman 🇨🇦 69.19 11 123.48 12 192.67
2019 12 Loena Hendrickx 🇧🇪 62.60 13 123.69 11 186.29
2019 13 Ekaterina Ryabova 🇦🇿 57.18 17 122.70 13 179.88
2019 14 Yi Christy Leung 🇭🇰 58.60 14 118.62 14 177.22
2019 15 Laurine Lecavelier 🇫🇷 56.81 19 113.78 15 170.59
2019 16 Nicole Schott 🇩🇪 63.18 12 107.38 17 170.56
2019 17 Alexandra Feigin 🇧🇬 56.69 20 108.62 16 165.31
2019 18 Daša Grm 🇸🇮 57.58 16 103.58 18 161.16
2019 19 Hongyi Chen 🇨🇳 58.53 15 99.06 19 157.59
2019 20 Eliska Brezinova 🇨🇿 57.13 18 96.32 20 153.45
2019 21 Natasha Mckay 🇬🇧 56.40 21 95.16 21 151.56
2019 22 Eva Lotta Kiibus 🇪🇪 55.38 23 94.61 22 149.99
2019 23 Alaine Chartrand 🇨🇦 55.89 22 93.08 23 148.97
2019 24 Isadora Williams 🇧🇷 55.20 24 88.02 24 143.22
2019 25 Ivett Tóth 🇭🇺 54.87 25 NA NA NA
2019 26 Pernille Sørensen 🇩🇰 54.36 26 NA NA NA
2019 27 Marina Piredda 🇮🇹 53.27 27 NA NA NA
2019 28 Emmi Peltonen 🇫🇮 53.22 28 NA NA NA
2019 29 Julia Sauter 🇷🇴 53.11 29 NA NA NA
2019 30 Anita Östlund 🇸🇪 53.07 30 NA NA NA
2019 31 Roberta Rodeghiero 🇮🇹 51.50 31 NA NA NA
2019 32 Nicole Rajičová 🇸🇰 51.22 32 NA NA NA
2019 33 Alexia Paganini 🇨🇭 50.51 33 NA NA NA
2019 34 Valentina Matos 🇪🇸 50.25 34 NA NA NA
2019 35 Aurora Cotop 🇨🇦 48.83 35 NA NA NA
2019 36 Kailani Craine 🇦🇺 48.82 36 NA NA NA
2019 37 Sophia Schaller 🇦🇹 48.72 37 NA NA NA
2019 38 Elzbieta Kropa 🇱🇹 47.95 38 NA NA NA
2019 39 Anastasiya Galustyan 🇦🇲 47.75 39 NA NA NA
2019 40 Kyarha Van Tiel 🇳🇱 41.85 40 NA NA NA
2018 1 Nathan Chen 🇺🇸 101.94 1 219.46 1 321.40
2018 2 Shoma Uno 🇯🇵 94.26 5 179.51 2 273.77
2018 3 Mikhail Kolyada 🇷🇺 100.08 2 172.24 4 272.32
2018 4 Alexei Bychenko 🇮🇱 90.99 7 167.29 7 258.28
2018 5 Kazuki Tomono 🇯🇵 82.61 11 173.50 3 256.11
2018 6 Deniss Vasiljevs 🇱🇻 84.25 9 170.61 5 254.86
2018 7 Dmitri Aliev 🇷🇺 82.15 13 170.15 6 252.30
2018 8 Keegan Messing 🇨🇦 93.00 6 159.30 11 252.30
2018 9 Misha Ge 🇺🇿 86.01 8 163.56 9 249.57
2018 10 Michal Březina 🇨🇿 78.01 17 165.98 8 243.99
2018 11 Max Aaron 🇺🇸 79.78 15 161.71 10 241.49
2018 12 Alexander Majorov 🇸🇪 82.71 10 155.08 13 237.79
2018 13 Keiji Tanaka 🇯🇵 80.17 14 156.49 12 236.66
2018 14 Vincent Zhou 🇺🇸 96.78 3 138.46 19 235.24
2018 15 Paul Fentz 🇩🇪 82.49 12 148.43 16 230.92
2018 16 Romain Ponsart 🇫🇷 79.55 16 149.65 14 229.20
2018 17 Matteo Rizzo 🇮🇹 77.43 18 148.01 17 225.44
2018 18 Brendan Kerry 🇦🇺 74.99 19 148.86 15 223.85
2018 19 Boyang Jin 🇨🇳 95.85 4 127.56 23 223.41
2018 20 Daniel Samohin 🇮🇱 72.78 20 141.23 18 214.01
2018 21 Julian Zhi-Jie Yee 🇲🇾 72.43 21 136.60 20 209.03
2018 22 Donovan Carrillo 🇲🇽 68.13 24 132.63 21 200.76
2018 23 Slavik Hayrapetyan 🇦🇲 68.18 23 131.54 22 199.72
2018 24 Phillip Harris 🇬🇧 68.59 22 119.10 24 187.69
2018 25 Nam Nguyen 🇨🇦 67.79 25 NA NA NA
2018 26 Morisi Kvitelashvili 🇬🇪 67.01 26 NA NA NA
2018 27 Stéphane Walker 🇨🇭 65.79 27 NA NA NA
2018 28 Burak Demirboga 🇹🇷 65.43 28 NA NA NA
2018 29 Ivan Pavlov 🇺🇦 64.18 29 NA NA NA
2018 30 Chih-I Tsao 🇹🇼 64.06 30 NA NA NA
2018 31 Larry Loupolover 🇦🇿 61.82 31 NA NA NA
2018 32 Abzal Rakimgaliev 🇰🇿 61.19 32 NA NA NA
2018 33 Jinseo Kim 🇰🇷 60.72 33 NA NA NA
2018 34 Nicholas Vrdoljak 🇭🇷 59.74 34 NA NA NA
2018 35 Valtter Virtanen 🇫🇮 55.49 35 NA NA NA
2018 36 Ihor Reznichenko 🇵🇱 51.70 36 NA NA NA
2018 37 Javier Raya 🇪🇸 50.00 37 NA NA NA
2018 1 Kaetlyn Osmond 🇨🇦 72.73 4 150.50 1 223.23
2018 2 Wakaba Higuchi 🇯🇵 65.89 8 145.01 2 210.90
2018 3 Satoko Miyahara 🇯🇵 74.36 3 135.72 3 210.08
2018 4 Carolina Kostner 🇮🇹 80.27 1 128.61 5 208.88
2018 5 Alina Zagitova 🇷🇺 79.51 2 128.21 7 207.72
2018 6 Bradie Tennell 🇺🇸 68.76 7 131.13 4 199.89
2018 7 Gabrielle Daleman 🇨🇦 71.61 6 125.11 8 196.72
2018 8 Maria Sotskova 🇷🇺 71.80 5 124.81 9 196.61
2018 9 Loena Hendrickx 🇧🇪 64.07 10 128.24 6 192.31
2018 10 Mirai Nagasu 🇺🇸 65.21 9 122.31 11 187.52
2018 11 Elizabet Tursynbayeva 🇰🇿 62.38 11 124.47 10 186.85
2018 12 Mariah Bell 🇺🇸 59.15 17 115.25 12 174.40
2018 13 Nicole Schott 🇩🇪 61.84 12 112.29 14 174.13
2018 14 Laurine Lecavelier 🇫🇷 59.79 15 113.44 13 173.23
2018 15 Hanul Kim 🇰🇷 60.14 14 110.54 15 170.68
2018 16 Viveca Lindfors 🇫🇮 60.18 13 106.05 16 166.23
2018 17 Kailani Craine 🇦🇺 56.90 20 97.51 18 154.41
2018 18 Eliska Brezinova 🇨🇿 58.37 18 94.77 19 153.14
2018 19 Stanislava Konstantinova 🇷🇺 59.19 16 93.84 20 153.03
2018 20 Alexia Paganini 🇨🇭 57.86 19 91.80 22 149.66
2018 21 Elisabetta Leccardi 🇮🇹 51.13 23 98.04 17 149.17
2018 22 Daša Grm 🇸🇮 52.43 22 92.08 21 144.51
2018 23 Ivett Tóth 🇭🇺 50.63 24 86.24 23 136.87
2018 NA Dabin Choi 🇰🇷 55.30 21 NA NA NA
2018 25 Larkyn Austman 🇨🇦 50.17 25 NA NA NA
2018 26 Xiangning Li 🇨🇳 50.06 26 NA NA NA
2018 27 Nicole Rajičová 🇸🇰 49.87 27 NA NA NA
2018 28 Amy Lin 🇹🇼 49.31 28 NA NA NA
2018 29 Anita Östlund 🇸🇪 48.99 29 NA NA NA
2018 30 Alisa Stomakhina 🇦🇹 48.71 30 NA NA NA
2018 31 Elzbieta Kropa 🇱🇹 46.53 31 NA NA NA
2018 32 Natasha Mckay 🇬🇧 45.89 32 NA NA NA
2018 33 Anne Line Gjersem 🇳🇴 45.25 33 NA NA NA
2018 34 Gerli Liinamäe 🇪🇪 45.14 34 NA NA NA
2018 35 Isadora Williams 🇧🇷 42.16 35 NA NA NA
2018 36 Antonina Dubinina 🇷🇸 41.40 36 NA NA NA
2018 37 Angelina Kuchvalska 🇱🇻 35.78 37 NA NA NA
2017 1 Yuzuru Hanyu 🇯🇵 98.39 5 223.20 1 321.59
2017 2 Shoma Uno 🇯🇵 104.86 2 214.45 2 319.31
2017 3 Boyang Jin 🇨🇳 98.64 4 204.94 3 303.58
2017 4 Javier Fernández 🇪🇸 109.05 1 192.14 6 301.19
2017 5 Patrick Chan 🇨🇦 102.13 3 193.03 5 295.16
2017 6 Nathan Chen 🇺🇸 97.33 6 193.39 4 290.72
2017 7 Jason Brown 🇺🇸 93.10 8 176.47 7 269.57
2017 8 Mikhail Kolyada 🇷🇺 93.28 7 164.19 9 257.47
2017 9 Kevin Reynolds 🇨🇦 84.44 12 169.40 8 253.84
2017 10 Alexei Bychenko 🇮🇱 85.28 11 160.68 12 245.96
2017 11 Maxim Kovtun 🇷🇺 89.38 10 156.46 14 245.84
2017 12 Misha Ge 🇺🇿 79.91 16 163.54 10 243.45
2017 13 Morisi Kvitelashvili 🇬🇪 76.34 19 162.90 11 239.24
2017 14 Deniss Vasiljevs 🇱🇻 81.73 14 157.27 13 239.00
2017 15 Brendan Kerry 🇦🇺 83.11 13 153.13 15 236.24
2017 16 Denis Ten 🇰🇿 90.18 9 144.13 20 234.31
2017 17 Chafik Besseghier 🇫🇷 78.82 17 151.31 16 230.13
2017 18 Michal Březina 🇨🇿 80.02 15 146.24 18 226.26
2017 19 Keiji Tanaka 🇯🇵 73.45 22 148.89 17 222.34
2017 20 Paul Fentz 🇩🇪 73.89 20 144.02 21 217.91
2017 21 Jorik Hendrickx 🇧🇪 73.68 21 140.34 22 214.02
2017 22 Julian Zhi-Jie Yee 🇲🇾 69.74 23 144.25 19 213.99
2017 23 Alexander Majorov 🇸🇪 77.23 18 127.81 23 205.04
2017 24 Michael Christian Martinez 🇵🇭 69.32 24 127.47 24 196.79
2017 25 Ivan Pavlov 🇺🇦 69.26 25 NA NA NA
2017 26 Jinseo Kim 🇰🇷 68.66 26 NA NA NA
2017 27 Javier Raya 🇪🇸 66.88 27 NA NA NA
2017 28 Stéphane Walker 🇨🇭 64.04 28 NA NA NA
2017 29 Ihor Reznichenko 🇵🇱 63.88 29 NA NA NA
2017 30 Matteo Rizzo 🇮🇹 63.14 30 NA NA NA
2017 31 Graham Newberry 🇬🇧 62.04 31 NA NA NA
2017 32 Chih-I Tsao 🇹🇼 61.52 32 NA NA NA
2017 33 Valtter Virtanen 🇫🇮 59.45 33 NA NA NA
2017 34 Nicholas Vrdoljak 🇭🇷 57.28 34 NA NA NA
2017 35 Slavik Hayrapetyan 🇦🇲 57.14 35 NA NA NA
2017 36 Larry Loupolover 🇦🇿 38.97 36 NA NA NA
2017 1 Evgenia Medvedeva 🇷🇺 79.01 1 154.40 1 233.41
2017 2 Kaetlyn Osmond 🇨🇦 75.98 2 142.15 2 218.13
2017 3 Gabrielle Daleman 🇨🇦 72.19 3 141.33 3 213.52
2017 4 Karen Chen 🇺🇸 69.98 5 129.31 6 199.29
2017 5 Mai Mihara 🇯🇵 59.59 15 138.29 4 197.88
2017 6 Carolina Kostner 🇮🇹 66.33 8 130.50 5 196.83
2017 7 Ashley Wagner 🇺🇸 69.04 7 124.50 10 193.54
2017 8 Maria Sotskova 🇷🇺 69.76 6 122.44 11 192.20
2017 9 Elizabet Tursynbayeva 🇰🇿 65.48 10 126.51 8 191.99
2017 10 Dabin Choi 🇰🇷 62.66 11 128.45 7 191.11
2017 11 Wakaba Higuchi 🇯🇵 65.87 9 122.18 12 188.05
2017 12 Mariah Bell 🇺🇸 61.02 13 126.21 9 187.23
2017 13 Anna Pogorilaya 🇷🇺 71.52 4 111.85 15 183.37
2017 14 Xiangning Li 🇨🇳 58.28 16 117.09 13 175.37
2017 15 Loena Hendrickx 🇧🇪 57.54 17 115.28 14 172.82
2017 16 Rika Hongo 🇯🇵 62.55 12 107.28 18 169.83
2017 17 Nicole Rajičová 🇸🇰 57.08 18 108.47 16 165.55
2017 18 Laurine Lecavelier 🇫🇷 55.49 22 107.50 17 162.99
2017 19 Nicole Schott 🇩🇪 54.83 24 106.58 19 161.41
2017 20 Ivett Tóth 🇭🇺 61.00 14 99.77 21 160.77
2017 21 Zijun Li 🇨🇳 56.30 20 103.50 20 159.80
2017 22 Angelina Kuchvalska 🇱🇻 55.92 21 99.10 22 155.02
2017 23 Anastasiya Galustyan 🇦🇲 55.20 23 98.27 23 153.47
2017 24 Kailani Craine 🇦🇺 56.97 19 95.97 24 152.94
2017 25 Shuran Yu 🇸🇬 52.87 25 NA NA NA
2017 26 Joshi Helgesson 🇸🇪 52.07 26 NA NA NA
2017 27 Helery Hälvin 🇪🇪 51.94 27 NA NA NA
2017 28 Amy Lin 🇹🇼 51.86 28 NA NA NA
2017 29 Emmi Peltonen 🇫🇮 50.74 29 NA NA NA
2017 30 Isadora Williams 🇧🇷 50.65 30 NA NA NA
2017 31 Kerstin Frank 🇦🇹 50.54 31 NA NA NA
2017 32 Natasha Mckay 🇬🇧 50.10 32 NA NA NA
2017 33 Yasmine Kimiko Yamada 🇨🇭 47.86 33 NA NA NA
2017 34 Anne Line Gjersem 🇳🇴 46.99 34 NA NA NA
2017 35 Anna Khnychenkova 🇺🇦 46.98 35 NA NA NA
2017 36 Daša Grm 🇸🇮 46.63 36 NA NA NA
2017 37 Michaela Hanzlikova 🇨🇿 32.21 37 NA NA NA

Missing Data

Let’s explore if there are any missing data in our datasets first. Here is a missing data plot for our regular competition score dataset:

reg_merged %>% 
  vis_miss()

And for the Worlds dataset:

worldmerged %>% 
  vis_miss()

There seems to be a lot of missing data within the Free Skate score and rank, and the total score and final rank. Some of this could be because at bigger events, like the European Championships, 4 Continents championships, and Worlds Championships, only the top 24 skaters can advanced to the free skate. This leaves them with no free skate score or rank, and also doesn’t count towards their total score since they technically did not finish the competition. Here is an example below from the 2023 European Championships for women, where those ranked below 24 have missing data.

year final_rank skater nation sp_score sp_rank fs_score fs_rank total_score competition sex
2023 25 Alexandra Michaela Filcová SK 43.94 25 NA NA NA EC Female
2023 26 Léa Serna FR 43.93 26 NA NA NA EC Female
2023 27 Antonina Dubinina RS 42.51 27 NA NA NA EC Female
2023 28 Anastasia Gracheva MD 39.08 28 NA NA NA EC Female
2023 29 Alexandra Mintsidou GR 33.86 29 NA NA NA EC Female

A missing final rank could be a skater withdrawing from the competition. An example would be Isabeau Levito during 2023 4 Continents. She did her short program but withdrew after because of sickness and therefore does not get ranked.

reg_merged %>% 
  filter(skater == 'Isabeau Levito', competition == '4CC', year == 2023)%>% 
  kable() %>% 
  kable_styling(full_width = F)
year final_rank skater nation sp_score sp_rank fs_score fs_rank total_score competition sex
2023 NA Isabeau Levito US 71.5 2 NA NA NA 4CC Female

For the dataset of Worlds score, we see the same issues for missing data; however, I filled in their total score with their short program score if they reached that point since we need it for total World score as the response variable. We can say the free skate score was 0 since they did not get to do it, so their total score is just their short program score and they are ranked based on that. We can see this below with how the Worlds score between 24th and 25th place has a huge drop.

#fix missing total score
worldmerged$worldscore <- ifelse(is.na(worldmerged$worldscore), worldmerged$sp_score, worldmerged$worldscore)

#example
worldmerged %>% 
  filter(year == 2018, worldrank <=25, worldrank>=24, skater != "Larkyn Austman") %>% 
  kable() %>% 
  kable_styling(full_width = F) %>% 
  scroll_box(width = "100%", height = "200px")
year worldrank skater nation sp_score sp_rank fs_score fs_rank worldscore
2018 24 Phillip Harris 🇬🇧 68.59 22 119.1 24 187.69
2018 25 Nam Nguyen 🇨🇦 67.79 25 NA NA 67.79

Tidying Data

First, I decided that I would separate average short program score and average free skate score, rather than just using average total score, for my analysis. This is because sometime skaters can do better in one program compared to the other. Free skates allow for higher scores to be earned, so if a skater typically falters during a free skate, we would like to see that reflected. It also helps our missing data problem, where any missing free skate scores won’t have too much of an effect as long as the skater has another free skate score in the season or the skater doesn’t reach the free skate during Worlds.

So I added 2 columns into the regular competition score data set that holds the average scores for each skater for that season. Then I pivoted the dataframe so that each competition has its own column and is marked 1 if the skater attended the event that season and a 0 if not. Finally, I merged the regular score dataframe with the Worlds score dataframe, dropped unneeded columns, and finally got for each season, each skater has one row with their name, the ending year of the season as a factor, their sex, their short program average, their free program average, factor variables that tells us which competition they went to in the season, and for the response variable for our predictions: their score at Worlds that year.

#take averages
avg <- reg_merged %>% 
  group_by(skater, year) %>% 
  mutate(spavg = mean(sp_score,na.rm = TRUE)) %>% 
  mutate(fpavg = mean(fs_score, na.rm=TRUE)) %>% 
  mutate(tsavg = mean(total_score, na.rm=TRUE)) %>%
  select(skater, year, competition, spavg, fpavg, tsavg, sex)

#pivot competition variable
avg$truth <- 1
pivoted <- avg %>% 
  pivot_wider(names_from = competition, values_from = truth)
pivoted[is.na(pivoted)] <- 0

#drop unneeded columns in worldmerged
worldmerged <- worldmerged %>% 
  select(year, skater, worldscore, worldrank)

#for graphing later on
graphdata <- merge(worldmerged, pivoted, by = c('skater', 'year'))
graphdata$tsavg <- ifelse(graphdata$tsavg==0, graphdata$spavg, graphdata$tsavg)

#final dataframe for analysis
data <- graphdata %>% 
  select(-tsavg, - worldrank) %>% 
  mutate(year = as.factor(year))

#factor competition variables
for (i in c(7:19)) {
  data[, i] <- factor(data[, i])
}

data %>% kable() %>% 
  kable_styling(full_width = F) %>% 
  scroll_box(width = "100%", height = "200px")
skater year worldscore spavg fpavg sex GPUSA GPJPN GPGBR GPFRA GPFIN GPF GPCAN EC 4CC OLY GPRUS GPITA GPCHN
Abzal Rakimgaliev 2018 61.19 60.77000 114.81000 Male 0 0 0 0 0 0 0 0 1 0 0 0 0
Adam Hagara 2022 60.92 65.23000 0.00000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Adam Hagara 2023 203.26 65.15000 124.57000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Adam Siao Him Fa 2022 266.12 79.60333 157.38333 Male 1 0 0 1 0 0 0 0 0 1 0 0 0
Adam Siao Him Fa 2023 253.11 90.65667 171.74333 Male 0 1 0 1 0 0 0 1 0 0 0 0 0
Alaine Chartrand 2019 148.97 51.11667 107.28333 Female 1 0 0 0 0 0 1 0 1 0 0 0 0
Aleksandr Selevko 2019 63.25 69.94000 125.19000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Alexander Majorov 2017 205.04 73.33500 131.72500 Male 0 0 0 0 0 0 0 1 0 0 1 0 0
Alexander Majorov 2018 237.79 67.77500 138.17500 Male 0 0 0 0 0 0 0 1 0 0 0 0 1
Alexander Majorov 2019 229.72 82.28333 134.80667 Male 0 0 0 0 0 0 1 1 0 0 1 0 0
Alexander Samarin 2019 246.33 90.29667 164.94000 Male 0 0 0 1 0 0 1 1 0 0 0 0 0
Alexandra Feigin 2019 165.31 58.80000 105.40000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Alexandra Feigin 2022 55.01 57.97000 99.46500 Female 0 0 0 0 0 0 0 1 0 1 0 0 0
Alexandra Feigin 2023 155.74 54.31000 100.92000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Alexei Bychenko 2017 245.96 82.87333 158.67000 Male 0 1 0 0 0 0 0 1 0 0 1 0 0
Alexei Bychenko 2018 258.28 82.85250 165.88750 Male 0 1 0 1 0 0 0 1 0 1 0 0 0
Alexei Bychenko 2019 216.60 75.77333 130.99333 Male 1 0 0 0 1 0 0 1 0 0 0 0 0
Alexia Paganini 2018 149.66 55.10500 103.83500 Female 0 0 0 0 0 0 0 1 0 1 0 0 0
Alexia Paganini 2019 50.51 61.98333 110.98667 Female 0 0 0 1 0 0 0 1 0 0 1 0 0
Alexia Paganini 2022 170.02 61.69000 111.81500 Female 0 0 0 0 0 0 0 1 0 1 0 0 0
Alina Zagitova 2018 207.72 74.27200 151.48600 Female 0 0 0 1 0 1 0 1 0 1 0 0 1
Alina Zagitova 2019 237.50 75.65250 140.12500 Female 0 0 0 0 1 1 0 1 0 0 1 0 0
Alysa Liu 2022 211.19 70.28333 135.84333 Female 0 1 0 0 0 0 1 0 0 1 0 0 0
Amber Glenn 2023 188.33 63.36333 123.12667 Female 1 1 0 0 0 0 0 0 1 0 0 0 0
Amy Lin 2017 51.86 45.40000 79.62000 Female 0 0 0 0 0 0 0 0 1 0 0 0 0
Amy Lin 2018 49.31 51.14000 86.26000 Female 0 0 0 0 0 0 0 0 1 0 0 0 0
Anastasia Gracheva 2023 50.55 39.08000 0.00000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Anastasiia Gubanova 2022 196.61 66.21000 128.36500 Female 0 0 0 0 0 0 0 1 0 1 0 0 0
Anastasiia Gubanova 2023 184.92 64.22000 122.31000 Female 0 0 1 0 1 0 0 1 0 0 0 0 0
Anastasiya Galustyan 2017 153.47 56.41667 100.21333 Female 0 0 0 1 0 0 0 1 0 0 1 0 0
Anastasiya Galustyan 2019 47.75 48.38000 84.25000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Andreas Nordebäck 2023 223.52 75.98000 136.97000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Andrei Lazukin 2019 248.74 72.49500 144.50500 Male 0 0 0 0 1 0 0 0 0 0 1 0 0
Anete Lace 2022 44.60 49.75000 0.00000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Angelina Kuchvalska 2017 155.02 50.38000 91.51667 Female 1 0 0 0 0 0 0 1 0 0 1 0 0
Anita Östlund 2018 48.99 52.59000 89.10000 Female 0 0 0 0 0 0 0 1 0 1 0 0 0
Anita Östlund 2019 53.07 52.76000 91.90000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Anna Khnychenkova 2017 46.98 48.93000 87.64000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Anna Pogorilaya 2017 183.37 73.29250 140.19000 Female 0 1 0 0 0 1 0 1 0 0 1 0 0
Anne Line Gjersem 2017 46.99 48.06000 80.62000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Anne Line Gjersem 2018 45.25 48.70000 93.98000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Antonina Dubinina 2018 41.40 36.69000 0.00000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Ashley Wagner 2017 193.54 66.93000 121.98000 Female 1 0 0 0 0 0 0 0 0 0 0 0 1
Boyang Jin 2017 303.58 86.81000 176.90000 Male 1 0 0 0 0 0 0 0 1 0 0 0 1
Boyang Jin 2018 223.41 93.83750 183.47000 Male 1 0 0 0 0 0 0 0 1 1 0 0 1
Boyang Jin 2019 262.71 85.85000 150.71000 Male 0 0 0 1 1 0 0 0 1 0 0 0 0
Boyang Jin 2023 204.22 85.32000 142.15000 Male 0 0 0 0 0 0 0 0 1 0 0 0 0
Bradie Tennell 2018 199.89 65.51000 132.71500 Female 1 0 0 0 0 0 0 0 0 1 0 0 0
Bradie Tennell 2019 213.47 65.65667 131.92333 Female 1 0 0 1 0 0 0 0 1 0 0 0 0
Bradie Tennell 2023 184.14 62.21000 110.15000 Female 0 0 1 0 1 0 0 0 1 0 0 0 0
Brendan Kerry 2017 236.24 73.46667 139.38333 Male 1 0 0 1 0 0 0 0 1 0 0 0 0
Brendan Kerry 2018 223.85 75.27333 143.16667 Male 0 0 0 0 0 0 1 0 1 1 0 0 0
Brendan Kerry 2019 222.02 74.34000 139.69667 Male 0 0 0 0 0 0 1 0 1 0 1 0 0
Burak Demirboga 2018 65.43 61.27000 105.95000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Burak Demirboga 2019 60.79 56.95000 0.00000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Burak Demirboga 2022 52.86 67.30000 100.73000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Burak Demirboga 2023 65.73 64.33000 118.49000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Camden Pulkinen 2022 271.69 65.52667 146.32000 Male 0 1 0 0 0 0 0 0 1 0 1 0 0
Carolina Kostner 2017 196.83 72.40000 138.12000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Carolina Kostner 2018 208.88 74.69200 137.22000 Female 0 1 0 0 0 1 0 1 0 1 1 0 0
Chaeyeon Kim 2023 203.51 71.39000 131.00000 Female 0 0 0 0 0 0 0 0 1 0 0 0 0
Chafik Besseghier 2017 230.13 77.95667 147.57333 Male 0 0 0 1 0 0 0 1 0 0 1 0 0
Chih-I Tsao 2017 61.52 51.02000 118.61000 Male 0 0 0 0 0 0 0 0 1 0 0 0 0
Chih-I Tsao 2018 64.06 72.57000 122.64000 Male 0 0 0 0 0 0 0 0 1 0 0 0 0
Conrad Orzel 2023 67.65 74.29333 133.77667 Male 0 1 0 0 0 0 1 0 1 0 0 0 0
Dabin Choi 2017 191.11 55.32333 115.95000 Female 0 1 0 0 0 0 1 0 1 0 0 0 0
Dabin Choi 2018 55.30 61.32333 123.83667 Female 0 0 0 0 0 0 0 0 1 1 0 0 1
Daniel Grassl 2022 266.66 87.23500 173.51000 Male 1 0 0 0 0 0 0 1 0 1 0 1 0
Daniel Grassl 2023 244.43 83.17750 166.28000 Male 1 0 1 0 0 1 0 1 0 0 0 0 0
Daniel Samohin 2018 214.01 71.04250 146.26000 Male 1 0 0 0 0 0 0 1 0 1 1 0 0
Daniel Samohin 2019 205.28 81.23667 135.11333 Male 0 0 0 1 0 0 1 1 0 0 0 0 0
Daša Grm 2017 46.63 43.48000 0.00000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Daša Grm 2018 144.51 47.40000 89.91000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Daša Grm 2019 161.16 53.50000 93.79000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Daša Grm 2022 147.12 47.85000 0.00000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Daša Grm 2023 47.04 52.47000 90.58000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Denis Ten 2017 234.31 89.21000 180.05000 Male 0 0 0 1 0 0 0 0 0 0 0 0 0
Deniss Vasiljevs 2017 239.00 70.92333 149.97667 Male 0 1 0 0 0 0 0 1 0 0 1 0 0
Deniss Vasiljevs 2018 254.86 80.89500 154.21250 Male 0 1 0 0 0 0 0 1 0 1 1 0 0
Deniss Vasiljevs 2019 218.52 77.85333 134.93333 Male 0 1 0 1 0 0 0 1 0 0 0 0 0
Deniss Vasiljevs 2022 243.00 87.59750 169.33000 Male 0 0 0 1 0 0 0 1 0 1 0 1 0
Deniss Vasiljevs 2023 243.15 78.94333 150.51000 Male 0 0 1 0 0 0 1 1 0 0 0 0 0
Dmitri Aliev 2018 252.30 89.14750 162.01000 Male 0 1 0 0 0 0 0 1 0 1 1 0 0
Donovan Carrillo 2018 200.76 59.07000 126.84000 Male 0 0 0 0 0 0 0 0 1 0 0 0 0
Donovan Carrillo 2019 54.99 71.16000 103.54000 Male 0 0 0 0 0 0 0 0 1 0 0 0 0
Ekaterina Kurakova 2022 186.43 61.08500 127.61750 Female 1 0 0 0 0 0 0 1 0 1 1 0 0
Ekaterina Kurakova 2023 181.43 62.97333 122.36667 Female 1 0 1 0 0 0 0 1 0 0 0 0 0
Ekaterina Ryabova 2019 179.88 59.95000 103.22000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Ekaterina Ryabova 2022 188.50 62.37500 122.27750 Female 0 0 0 1 0 0 0 1 0 1 1 0 0
Eliska Brezinova 2018 153.14 52.06000 97.63000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Eliska Brezinova 2019 153.45 55.85000 110.92000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Eliska Brezinova 2022 55.07 61.96500 103.36000 Female 0 0 0 0 0 0 0 1 0 1 0 0 0
Eliska Brezinova 2023 47.29 55.89500 100.40500 Female 1 0 0 0 0 0 1 0 0 0 0 0 0
Elizabet Tursynbayeva 2017 191.99 62.28000 115.41333 Female 0 1 0 0 0 0 0 0 1 0 1 0 0
Elizabet Tursynbayeva 2018 186.85 60.38750 119.42250 Female 0 0 0 1 0 0 0 0 1 1 1 0 0
Elizabet Tursynbayeva 2019 224.76 63.67000 127.53667 Female 0 0 0 0 0 0 1 0 1 0 1 0 0
Elzbieta Kropa 2018 46.53 46.06000 87.81000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Emmi Peltonen 2017 50.74 53.52000 107.05000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Emmi Peltonen 2019 53.22 58.98000 105.39500 Female 0 0 0 0 1 0 0 1 0 0 0 0 0
Eunsoo Lim 2019 205.57 65.56000 125.71667 Female 0 1 0 0 0 0 0 0 1 0 1 0 0
Evgenia Medvedeva 2017 233.41 78.22250 146.66750 Female 0 0 0 1 0 1 1 1 0 0 0 0 0
Evgenia Medvedeva 2019 223.80 64.19000 131.17000 Female 0 0 0 1 0 0 1 0 0 0 0 0 0
Gabrielle Daleman 2017 213.52 68.48000 123.40000 Female 1 0 0 1 0 0 0 0 1 0 0 0 0
Gabrielle Daleman 2018 196.72 69.21000 116.93333 Female 1 0 0 0 0 0 0 0 0 1 0 0 1
Georgiy Reshtenko 2023 59.93 54.52000 0.00000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Graham Newberry 2017 62.04 67.79000 130.27000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Graham Newberry 2022 210.40 64.49000 0.00000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Graham Newberry 2023 61.70 67.57500 109.95500 Male 0 0 1 0 0 0 0 1 0 0 0 0 0
Haein Lee 2022 196.55 65.26000 126.35333 Female 0 0 0 1 0 0 1 0 1 0 0 0 0
Haein Lee 2023 220.94 66.04667 128.56333 Female 1 0 0 1 0 0 0 0 1 0 0 0 0
Hanul Kim 2018 170.68 57.74000 116.66500 Female 0 0 0 0 0 0 0 0 1 1 0 0 0
Helery Hälvin 2017 51.94 51.72000 94.96000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Hongyi Chen 2019 157.59 54.44000 96.06000 Female 0 0 0 0 0 0 0 0 1 0 0 0 0
Ihor Reznichenko 2017 63.88 54.81000 0.00000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Ihor Reznichenko 2018 51.70 63.96000 101.69000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Ihor Reznichenko 2019 50.15 59.99000 0.00000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Ilia Malinin 2023 288.44 83.91667 192.98333 Male 1 0 0 0 1 1 0 0 0 0 0 0 0
Isabeau Levito 2023 207.65 71.03000 135.67000 Female 1 0 1 0 0 1 0 0 1 0 0 0 0
Isadora Williams 2018 42.16 55.74000 88.44000 Female 0 0 0 0 0 0 0 0 0 1 0 0 0
Isadora Williams 2019 143.22 47.92000 90.34000 Female 0 0 0 0 0 0 0 0 1 0 0 0 0
Ivan Pavlov 2017 69.26 68.94000 133.93000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Ivan Shmuratko 2019 62.99 67.26000 111.03000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Ivan Shmuratko 2022 196.65 80.12000 130.04500 Male 0 0 0 0 0 0 0 1 0 1 0 0 0
Ivett Tóth 2017 160.77 61.49000 111.16000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Ivett Tóth 2018 136.87 51.96000 97.74500 Female 0 0 0 0 0 0 0 1 0 1 0 0 0
Ivett Tóth 2019 54.87 54.90000 105.93000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Janna Jyrkinen 2023 160.91 51.83000 113.87500 Female 0 0 0 0 1 0 0 1 0 0 0 0 0
Jari Kessler 2023 61.94 67.87000 114.96000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Jason Brown 2017 269.57 80.28333 163.95000 Male 1 1 0 0 0 0 0 0 1 0 0 0 0
Jason Brown 2019 254.15 86.48000 163.58333 Male 0 0 0 1 0 0 1 0 1 0 0 0 0
Javier Fernández 2017 301.19 96.03250 189.46000 Male 0 0 0 1 0 1 0 1 0 0 1 0 0
Javier Raya 2017 66.88 66.67000 128.87000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Jenni Saarinen 2022 55.30 57.95000 98.73000 Female 0 0 0 0 0 0 0 1 0 1 0 0 0
Jinseo Kim 2017 68.66 64.26000 130.79000 Male 0 0 0 0 0 0 0 0 1 0 0 0 0
Jorik Hendrickx 2017 214.02 79.82000 152.82667 Male 1 0 0 1 0 0 0 1 0 0 0 0 0
Josefin Taljegård 2022 163.24 56.37500 106.06000 Female 0 0 0 0 0 0 0 1 0 1 0 0 0
Joshi Helgesson 2017 52.07 50.96667 98.12333 Female 0 0 0 0 0 0 1 1 0 0 0 0 1
Júlia Láng 2023 44.26 46.33000 83.95000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Julia Sauter 2019 53.11 54.29000 98.86000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Julia Sauter 2023 165.62 54.48000 103.96000 Female 0 0 1 0 0 0 0 1 0 0 0 0 0
Julian Zhi-Jie Yee 2017 213.99 72.21000 130.46000 Male 0 0 0 0 0 0 0 0 1 0 0 0 0
Julian Zhi-Jie Yee 2018 209.03 71.01500 129.23000 Male 0 0 0 0 0 0 0 0 1 1 0 0 0
Julian Zhi-Jie Yee 2019 205.97 67.70667 119.06667 Male 1 0 0 0 0 0 0 0 1 0 1 0 0
Junhwan Cha 2019 229.26 89.52000 164.80000 Male 0 0 0 0 1 1 1 0 1 0 0 0 0
Junhwan Cha 2022 82.43 97.48750 168.24750 Male 0 1 0 0 0 0 0 0 1 1 0 1 0
Junhwan Cha 2023 296.03 86.18667 170.13000 Male 1 1 0 0 0 0 0 0 1 0 0 0 0
Kaetlyn Osmond 2017 218.13 72.57000 127.19750 Female 0 0 0 0 0 1 1 0 1 0 0 0 1
Kaetlyn Osmond 2018 223.23 75.25500 141.21000 Female 0 0 0 1 0 1 1 0 0 1 0 0 0
Kailani Craine 2017 152.94 54.70000 82.21000 Female 0 0 0 0 0 0 0 0 1 0 0 0 0
Kailani Craine 2018 154.41 54.17333 96.72000 Female 0 0 0 0 0 0 1 0 1 1 0 0 0
Kailani Craine 2019 48.82 59.42500 92.44500 Female 0 1 0 0 0 0 0 0 1 0 0 0 0
Kailani Craine 2022 161.75 53.69500 106.56000 Female 0 0 0 0 0 0 0 0 1 1 0 0 0
Kaori Sakamoto 2019 222.83 68.03500 139.41250 Female 1 0 0 0 1 1 0 0 1 0 0 0 0
Kaori Sakamoto 2022 236.09 75.85333 148.28000 Female 1 1 0 0 0 0 0 0 0 1 0 0 0
Kaori Sakamoto 2023 224.61 71.88333 132.13000 Female 1 1 0 0 0 1 0 0 0 0 0 0 0
Karen Chen 2017 199.29 57.54667 117.34000 Female 0 1 0 0 0 0 0 0 1 0 0 0 1
Karen Chen 2022 192.51 65.84000 119.94000 Female 0 0 0 1 0 0 1 0 0 1 0 0 0
Kazuki Tomono 2018 256.11 79.88000 152.05000 Male 0 1 0 0 0 0 0 0 0 0 0 0 0
Kazuki Tomono 2022 269.37 92.27333 167.15667 Male 0 0 0 0 0 0 0 0 1 0 1 1 0
Kazuki Tomono 2023 273.41 87.26500 163.03500 Male 0 1 0 1 0 0 0 0 0 0 0 0 0
Keegan Messing 2018 252.30 82.47000 153.85667 Male 0 1 0 0 0 0 1 0 0 1 0 0 0
Keegan Messing 2019 237.64 84.15500 163.24000 Male 0 0 0 0 0 1 1 0 1 0 1 0 0
Keegan Messing 2022 235.03 90.51667 161.82000 Male 0 0 0 1 0 0 1 0 0 1 0 0 0
Keegan Messing 2023 265.16 82.17000 161.60000 Male 0 0 0 0 1 0 1 0 1 0 0 0 0
Keiji Tanaka 2017 222.34 75.72333 155.45333 Male 0 1 0 0 0 0 0 0 1 0 1 0 0
Keiji Tanaka 2018 236.66 85.97333 164.79667 Male 0 0 0 0 0 0 0 0 1 1 0 0 1
Keiji Tanaka 2019 238.40 81.29333 143.60000 Male 0 0 0 1 1 0 0 0 1 0 0 0 0
Kerstin Frank 2017 50.54 51.47000 80.61000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Kévin Aymoz 2019 247.47 82.61667 153.24667 Male 0 0 0 1 0 0 1 1 0 0 0 0 0
Kévin Aymoz 2022 245.46 73.87750 165.90667 Male 1 0 0 1 0 0 0 1 0 1 0 0 0
Kévin Aymoz 2023 282.97 86.35500 161.95000 Male 0 0 0 0 1 0 0 1 0 0 0 0 0
Kevin Reynolds 2017 253.84 78.46500 155.22000 Male 0 0 0 0 0 0 1 0 1 0 0 0 0
Kimmy Repond 2023 194.09 63.83000 128.68000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Kyarha Van Tiel 2019 41.85 44.00000 0.00000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Kyrylo Marsak 2023 68.60 70.41000 111.57000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Lara Naki Gutmann 2022 164.39 53.88500 107.39500 Female 0 0 0 0 0 0 0 1 0 0 0 1 0
Lara Naki Gutmann 2023 178.43 55.39000 113.90000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Larkyn Austman 2018 50.17 46.60500 81.77000 Female 0 0 0 0 0 0 1 0 0 1 0 0 0
Larry Loupolover 2017 38.97 51.30000 0.00000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Larry Loupolover 2018 61.82 52.44000 0.00000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Laurine Lecavelier 2017 162.99 65.21000 121.16500 Female 0 0 0 1 0 0 0 1 0 0 0 0 0
Laurine Lecavelier 2018 173.23 58.37333 99.92333 Female 0 0 0 1 0 0 1 1 0 0 0 0 0
Laurine Lecavelier 2019 170.59 58.17333 111.72667 Female 1 0 0 1 0 0 0 1 0 0 0 0 0
Léa Serna 2022 54.30 62.45500 108.21000 Female 0 0 0 1 0 0 0 1 0 0 0 0 0
Lindsay Van Zundert 2022 171.39 54.08000 116.57000 Female 0 0 0 0 0 0 0 1 0 1 0 0 0
Lindsay Van Zundert 2023 159.55 56.15333 101.56333 Female 0 0 0 1 0 0 1 1 0 0 0 0 0
Loena Hendrickx 2017 172.82 55.41000 117.30000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Loena Hendrickx 2018 192.31 55.14500 119.25000 Female 0 0 0 0 0 0 0 1 0 1 0 0 0
Loena Hendrickx 2019 186.29 58.65000 128.05000 Female 1 0 0 0 1 0 0 0 0 0 0 0 0
Loena Hendrickx 2022 217.70 71.07500 138.30000 Female 0 0 0 0 0 0 0 1 0 1 1 1 0
Loena Hendrickx 2023 210.42 72.43000 130.09000 Female 0 0 0 1 1 1 0 1 0 0 0 0 0
Luc Maierhofer 2019 65.78 63.63000 125.37000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Lukas Britschgi 2019 54.58 55.86000 0.00000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Lukas Britschgi 2023 257.34 72.62000 155.14667 Male 0 0 0 1 0 0 1 1 0 0 0 0 0
Madeline Schizas 2022 188.14 63.65333 121.10000 Female 0 0 0 0 0 0 1 0 0 1 1 0 0
Madeline Schizas 2023 187.49 64.40000 111.65333 Female 0 0 0 0 1 0 1 0 1 0 0 0 0
Mai Mihara 2017 197.88 66.91333 126.77000 Female 1 0 0 0 0 0 0 0 1 0 0 0 1
Mai Mihara 2023 205.70 73.46333 136.45000 Female 0 0 1 0 1 1 0 0 0 0 0 0 0
Mana Kawabe 2022 182.44 63.29000 122.94000 Female 0 1 0 0 0 0 1 0 0 1 0 0 0
Maria Sotskova 2017 192.20 69.14500 127.74000 Female 0 1 0 1 0 1 0 1 0 0 0 0 0
Maria Sotskova 2018 196.61 68.09000 135.20800 Female 0 0 0 1 0 1 1 1 0 1 0 0 0
Mariah Bell 2017 187.23 61.06500 123.28000 Female 1 0 0 0 0 0 0 0 1 0 0 0 0
Mariah Bell 2018 174.40 61.33333 118.81333 Female 0 1 0 0 0 0 0 0 1 0 1 0 0
Mariah Bell 2019 208.07 65.44667 128.93667 Female 0 1 0 0 0 0 1 0 1 0 0 0 0
Mariah Bell 2022 208.66 65.18667 135.96000 Female 0 0 0 1 0 0 0 0 0 1 1 0 0
Marilena Kitromilis 2022 53.32 44.03000 0.00000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Marilena Kitromilis 2023 48.92 49.86000 97.33500 Female 1 0 0 0 0 0 0 1 0 0 0 0 0
Mark Gorodnitsky 2023 232.13 64.94000 137.40000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Matteo Rizzo 2018 225.44 76.94500 148.97500 Male 0 0 0 0 0 0 0 1 0 1 0 0 0
Matteo Rizzo 2019 257.66 78.83333 153.70000 Male 1 1 0 0 0 0 0 1 0 0 0 0 0
Matteo Rizzo 2022 255.75 83.62000 167.66000 Male 0 1 0 0 0 0 0 0 0 1 1 0 0
Matteo Rizzo 2023 256.04 82.07000 168.50000 Male 0 1 0 0 0 0 1 1 0 0 0 0 0
Maurizio Zandrón 2022 228.27 70.75000 123.16000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Maurizio Zandrón 2023 194.31 70.39000 134.31000 Male 0 1 0 0 0 0 0 1 0 0 0 0 0
Max Aaron 2018 241.49 81.96667 168.81333 Male 0 0 0 1 0 0 0 0 1 0 0 0 1
Maxim Kovtun 2017 245.84 77.35333 162.30667 Male 1 0 0 0 0 0 0 1 0 0 0 0 1
Mia Caroline Risa Gomez 2023 43.54 49.14000 88.48000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Michael Christian Martinez 2017 196.79 72.47000 141.68000 Male 0 0 0 0 0 0 0 0 1 0 0 0 0
Michaela Hanzlikova 2017 32.21 52.39000 85.84000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Michal Březina 2017 226.26 74.94333 143.29333 Male 0 0 0 0 0 0 1 1 0 0 0 0 1
Michal Březina 2018 243.99 78.61250 153.57750 Male 0 1 0 0 0 0 1 1 0 1 0 0 0
Michal Březina 2019 254.28 87.06750 159.68250 Male 1 0 0 0 1 1 0 1 0 0 0 0 0
Mihhail Selevko 2023 230.94 76.29667 131.37667 Male 1 0 0 1 0 0 0 1 0 0 0 0 0
Mikhail Kolyada 2017 257.47 84.14000 156.25000 Male 0 1 0 0 0 0 0 1 0 0 1 0 0
Mikhail Kolyada 2018 272.32 91.64800 179.47000 Male 0 0 0 0 0 1 0 1 0 1 1 0 1
Mikhail Kolyada 2019 262.44 83.78333 151.24333 Male 0 0 0 0 1 0 0 1 0 0 1 0 0
Mikhail Shaidorov 2023 236.93 72.43000 164.71000 Male 0 0 0 0 0 0 0 0 1 0 0 0 0
Mirai Nagasu 2018 187.52 62.75000 123.66667 Female 0 1 0 0 0 0 0 0 0 1 1 0 0
Misha Ge 2017 243.45 75.54667 155.96667 Male 0 0 0 1 0 0 1 0 1 0 0 0 0
Misha Ge 2018 249.57 84.15000 167.74250 Male 0 0 0 1 0 0 0 0 1 1 1 0 0
Morisi Kvitelashvili 2017 239.24 76.85000 161.35000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Morisi Kvitelashvili 2018 67.01 80.23750 146.21250 Male 0 0 0 1 0 0 0 1 0 1 1 0 0
Morisi Kvitelashvili 2019 240.74 77.18667 147.31000 Male 1 0 0 0 0 0 0 1 0 0 1 0 0
Morisi Kvitelashvili 2022 272.03 89.42750 166.00500 Male 0 0 0 0 0 0 1 1 0 1 1 0 0
Morisi Kvitelashvili 2023 212.32 63.13000 132.41667 Male 0 0 1 0 1 0 0 1 0 0 0 0 0
Nam Nguyen 2018 67.79 76.88333 153.27667 Male 0 1 0 0 0 0 0 0 1 0 1 0 0
Nam Nguyen 2019 237.27 77.21000 146.26333 Male 1 0 0 0 0 0 1 0 1 0 0 0 0
Natasha Mckay 2017 50.10 45.97000 94.88000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Natasha Mckay 2018 45.89 45.12000 0.00000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Natasha Mckay 2019 151.56 48.20000 91.88000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Natasha Mckay 2022 159.27 54.80500 104.67000 Female 0 0 0 0 0 0 0 1 0 1 0 0 0
Nathan Chen 2017 290.72 92.30250 188.70250 Male 0 1 0 1 0 1 0 0 1 0 0 0 0
Nathan Chen 2018 321.40 97.56250 190.82000 Male 1 0 0 0 0 1 0 0 0 1 1 0 0
Nathan Chen 2019 323.42 90.17000 188.02000 Male 1 0 0 1 0 1 0 0 0 0 0 0 0
Nicholas Vrdoljak 2017 57.28 53.45000 0.00000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Nicholas Vrdoljak 2018 59.74 58.30000 0.00000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Nicole Rajičová 2017 165.55 57.44000 111.54667 Female 0 1 0 0 0 0 0 1 0 0 1 0 0
Nicole Rajičová 2018 49.87 57.59750 111.02250 Female 1 1 0 0 0 0 0 1 0 1 0 0 0
Nicole Rajičová 2019 51.22 64.08000 104.95000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Nicole Schott 2017 161.41 56.88000 103.75000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Nicole Schott 2018 174.13 54.66500 112.18750 Female 0 0 0 1 0 0 0 1 0 1 1 0 0
Nicole Schott 2019 170.56 50.68000 98.58000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Nicole Schott 2022 188.42 60.64500 111.20500 Female 0 1 0 0 0 0 0 1 0 1 0 1 0
Nicole Schott 2023 197.76 57.06000 111.46667 Female 1 0 1 0 0 0 0 1 0 0 0 0 0
Niina Petrõkina 2022 176.60 58.30000 128.77000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Niina Petrõkina 2023 193.49 60.51333 121.27667 Female 0 1 0 0 0 0 1 1 0 0 0 0 0
Nika Egadze 2023 65.17 79.95667 149.01333 Male 0 1 0 1 0 0 0 1 0 0 0 0 0
Nikita Starostin 2022 205.72 72.12000 142.28000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Nikita Starostin 2023 217.87 74.70000 123.27000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Nikolaj Majorov 2022 216.45 78.54000 142.24000 Male 0 0 0 0 0 0 0 0 0 1 0 0 0
Nina Pinzarrone 2023 191.78 61.35000 124.57000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Olga Mikutina 2022 182.98 59.46333 109.63667 Female 0 0 0 0 0 0 0 1 0 1 1 0 0
Olga Mikutina 2023 172.31 58.57667 105.56667 Female 0 1 0 1 0 0 0 1 0 0 0 0 0
Patrick Chan 2017 295.16 90.54750 179.80250 Male 0 0 0 0 0 1 1 0 1 0 0 0 1
Paul Fentz 2017 217.91 72.68000 153.17000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Paul Fentz 2018 230.92 71.91667 131.79000 Male 0 0 0 0 0 0 1 1 0 1 0 0 0
Paul Fentz 2019 63.24 73.99000 141.27500 Male 0 0 0 0 0 0 0 1 0 0 1 0 0
Pernille Sørensen 2019 54.36 50.59000 81.19000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Phillip Harris 2018 187.69 67.77000 140.45000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Rika Hongo 2017 169.83 62.84667 110.60667 Female 0 0 0 0 0 0 1 0 1 0 0 0 1
Rika Kihira 2019 223.49 72.14750 149.18750 Female 0 1 0 1 0 1 0 0 1 0 0 0 0
Rinka Watanabe 2023 192.81 64.95250 130.59000 Female 0 1 0 0 0 1 1 0 1 0 0 0 0
Romain Ponsart 2018 229.20 62.63000 136.79000 Male 0 0 0 1 0 0 0 1 0 0 0 0 0
Roman Sadovsky 2022 245.36 73.43333 157.00000 Male 0 0 0 0 0 0 1 0 0 1 1 0 0
Satoko Miyahara 2018 210.08 71.61200 138.13200 Female 1 1 0 0 0 1 0 0 1 1 0 0 0
Satoko Miyahara 2019 215.95 72.48667 141.01000 Female 1 1 0 0 0 1 0 0 0 0 0 0 0
Shoma Uno 2017 319.31 93.71000 190.03250 Male 1 0 0 0 0 1 0 0 1 0 1 0 0
Shoma Uno 2018 273.77 100.74200 192.31200 Male 0 0 0 1 0 1 1 0 1 1 0 0 0
Shoma Uno 2019 270.32 91.19750 188.28250 Male 0 1 0 0 0 1 1 0 1 0 0 0 0
Shoma Uno 2022 312.48 99.18333 185.42667 Male 1 1 0 0 0 0 0 0 0 1 0 0 0
Shoma Uno 2023 301.14 93.87667 191.91333 Male 0 1 0 0 0 1 1 0 0 0 0 0 0
Shuran Yu 2017 52.87 43.26000 75.14000 Female 0 0 0 0 0 0 0 0 1 0 0 0 0
Sihyeong Lee 2022 225.06 72.41000 144.05000 Male 0 0 0 0 0 0 0 0 1 1 0 0 0
Slavik Hayrapetyan 2017 57.14 60.69000 120.09000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Slavik Hayrapetyan 2018 199.72 69.49000 127.14000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Slavik Hayrapetyan 2019 60.66 59.87000 0.00000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Sofia Samodurova 2019 208.58 68.23250 135.48750 Female 1 0 0 0 0 1 0 1 0 0 1 0 0
Sofja Stepchenko 2023 158.38 55.32000 104.02000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Sophia Schaller 2019 48.72 44.20000 0.00000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Sota Yamamoto 2023 232.39 94.59000 168.77667 Male 0 1 0 1 0 1 0 0 0 0 0 0 0
Stéphane Walker 2017 64.04 62.86000 133.88000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Stéphane Walker 2018 65.79 65.96000 119.45000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Tomás Guarino Sabaté 2022 208.95 66.20000 112.47000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Tomás Guarino Sabaté 2023 67.60 71.65000 133.54000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Tzu-Han Ting 2022 55.24 49.15000 96.42000 Female 0 0 0 0 0 0 0 0 1 0 0 0 0
Valentina Matos 2019 50.25 42.86000 0.00000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Valtter Virtanen 2017 59.45 56.52000 107.57000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Valtter Virtanen 2018 55.49 60.23000 121.54000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Valtter Virtanen 2019 55.73 48.16000 106.58000 Male 0 0 0 0 1 0 0 0 0 0 0 0 0
Vincent Zhou 2018 235.24 76.96000 174.89333 Male 0 0 0 1 0 0 0 0 0 1 0 0 1
Vincent Zhou 2019 281.16 84.15333 156.31000 Male 1 1 0 0 0 0 0 0 1 0 0 0 0
Vincent Zhou 2022 277.38 98.47000 179.65500 Male 1 1 0 0 0 0 0 0 0 0 0 0 0
Viveca Lindfors 2018 166.23 51.62000 96.27000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Vladimir Litvintsev 2019 230.84 73.60000 130.68000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Vladimir Litvintsev 2022 233.62 83.80500 158.14000 Male 0 0 0 0 0 0 0 1 0 1 0 0 0
Vladimir Samoilov 2023 61.48 78.26000 113.33000 Male 0 0 0 0 0 0 0 1 0 0 0 0 0
Wakaba Higuchi 2017 188.05 62.14333 121.83000 Female 0 1 0 1 0 0 0 0 1 0 0 0 0
Wakaba Higuchi 2018 210.90 71.13000 136.13667 Female 0 0 0 0 0 1 0 0 0 0 1 0 1
Wakaba Higuchi 2022 188.15 68.93000 139.27667 Female 0 0 0 1 0 0 1 0 0 1 0 0 0
Xiangning Li 2017 175.37 55.14000 105.92000 Female 0 0 0 0 0 0 0 0 1 0 0 0 1
Xiangning Li 2018 50.06 55.97750 107.51500 Female 1 0 0 0 0 0 0 0 1 1 0 0 1
Yasmine Kimiko Yamada 2017 47.86 42.33000 0.00000 Female 0 0 0 0 0 0 0 1 0 0 0 0 0
Yelim Kim 2023 174.30 68.88500 128.39500 Female 0 1 0 1 0 1 0 0 1 0 0 0 0
Yi Christy Leung 2019 177.22 53.93000 110.86000 Female 0 0 0 0 0 0 0 0 1 0 0 0 0
Young You 2022 204.91 69.25250 138.80250 Female 1 1 0 0 0 0 0 0 1 1 0 0 0
Yuma Kagiyama 2022 297.60 96.43000 195.06333 Male 0 0 0 1 0 0 0 0 0 1 0 1 0
Yuzuru Hanyu 2017 321.59 96.77750 193.75750 Male 0 1 0 0 0 1 1 0 1 0 0 0 0
Yuzuru Hanyu 2019 300.97 108.61000 179.16000 Male 0 0 0 0 1 0 0 0 0 0 1 0 0
Zijun Li 2017 159.80 61.86000 115.23333 Female 0 0 0 0 0 0 0 0 1 0 1 0 1

Codebook

Here are the final variables I have selected to use in my dataframe for analysis:

  • skater: The skater’s full name

  • year: the ending year of the season that the competitions are a part of

  • worldscore: the total score of the skaters in the World Championships competition

  • spavg: a skater’s average short program score within that season

  • fpavg: a skater’s average free program score within that season

  • sex: the skater’s sex. Male or Female.

  • The competition variables: the event name abbreviation. A 1 indicates that the skater attended the event that season, and a 0 indicates that they did not. The abbreviations are as follows:

    • GPUSA: Grand Prix Skate America

    • GPCAN: Grand Prix Skate Canada

    • GPFRA: Grand Prix de France

    • GPCHN: Grand Prix Cup of China

    • GPFIN: Grand Prix Espoo/Helsinki

    • GPJPN: Grand Prix NHK Trophy

    • GPGBR: Grand Prix MK John Wilson Trophy

    • GPITA: Grand Prix Gran Premio d’Italia

    • GPRUS: Gran Prix Rostelecom Cup

    • GPF: Grand Prix Final

    • EC: European Championships

    • 4CC: 4 Continents Championships

    • OLY: Olympics

EDA

Scores Across Sexes

Let’s take a deeper look into the different variables. The first thing to note is that Men’s Singles tend to have higher score than Women’s because of the difference in difficulties between the categories. We can see that reflected in the histograms below, where we see the distribution of the Worlds Total scores between the 2 sexes. We can see their distribution between the 2 sexes differ a lot, so it is important during our analysis that we differ the 2 sexes from each other.

data %>%   
  ggplot(aes(x = worldscore, color = sex)) + 
  geom_histogram(alpha = 0.5, aes(fill = sex), position = 'identity')+
  labs(title = 'Scores Between Sexes at Worlds', x = 'Total Scores')+
  theme_bw()

We can see how the data clusters between those that did both the free and short program at Worlds, and those that did not qualify to the Free Program. For those that only participated in the short program, men seems to have a peak at around a score of 60, while women peak at around 50. For those that participated in both segments, we can see the bulk of the women’s score ranging from about 150 to 200, while the bulk of the men’s ranges from about 225 to 260. There is no limit to figure skating scores, but the world record in total scores for women’s is 272.71 and for men’s is 335.3, so we will expect those to be the maximum scores for the data we have.

Average Scores Across Years and Sexes

There is also a difference in the spread of scores between years for both segments. We can see the change in distribution between years for average short program scores and average free program scores below. For the free program scores, I filtered out any missing free program scores that were filled in as 0 so that we can get a more accurate look at the differences in scoring for the free program.

data %>%
  ggplot(aes(x = year, y = spavg, group = year, color = sex)) +
  geom_boxplot(fill = 'grey')+
  facet_wrap(~sex)+
  labs(title = 'Distribution of Avg. SP Score Between Years and Sexes', y = 'SP Score Averages', x = 'Year')+
  theme_bw()

data %>%
  filter(fpavg != 0) %>% 
  ggplot(aes(x = year, y = fpavg, group = year, color = sex)) +
  geom_boxplot(fill = 'grey')+
  facet_wrap(~sex)+
  labs(title = 'Distribution of Avg. FS Score Between Years and Sexes', y = 'FS Score Averages', x = 'Year')+
  theme_bw()

Figure skating has a lot of rule changes throughout the years, and these rule changes affect how a skater is able to earn points. Sometimes it might be easier for them to earn a few extra points or sometimes it could be harder to get an extra point boost over other skaters. These changes can be reflected in how the distribution of scores vary throughout the years, and it is why we should look at how the year affects a skater’s score. We can also see that average free program score varies more throughout the years compared to short program scores. The short programs have more rigorous rules that restricts a skater’s score-earning potential and the free program is when it is more open to point-earning but also the most affected by rule changes.

Number of Competitors

Different competitions have different amounts of competitors and ways to qualify for them. The 6 Grand Prix events are invite-only, usually allowing 10 to 12 competitors for each for Men’s and Women’s. A skater can be invited to 1 or 2 of these events. On top of those 6 events, the Grand Prix Final has the top 6 skaters for each gender in the GP events compete against each other. The 4 Continents, European Championships, and Olympics allow each ISU-recognized nation to have at least 1 skater enter the competition. A nation could earn up to 3 spots based on previous performances at other events. A skater also needs to have a minimum technical score (a component of their score for a program) to enter. Because of all of these rules, these 3 events can have varying amounts of competitors in them. We can see this broken down below with the number of competitors in each competition for the 2022 season.

reg_merged %>% 
  filter(year == 2022) %>% 
  ggplot(aes(y= competition))+
  geom_bar(aes(fill = sex))+
  facet_wrap(~sex)+
  labs(title = 'Number of Competitors')+
  theme_bw()

Scores Across Competitions

As mentioned before, there is a discrepancy with scoring between different competitions. Let’s take a look at the scores from the 2022-2023 season so we can see how the spread and median of scores differ.

reg_merged %>% 
  filter(year == 2023) %>% 
  ggplot(aes(x = competition, y = total_score, color = sex)) +
  geom_boxplot(fill = 'grey')+
  geom_jitter(alpha = 0.4)+
  facet_wrap(~sex)+
  labs(title = 'Distribution of Score across 2022-23 Competitions', x = 'Competition', y = 'Total Score')+
  theme_bw()+
  theme(axis.text.x=element_text(angle=90,hjust=1))

All the competitions have differing medians and ranges of scores. A popular fan opinion for this season was that GP Japan was relatively easy on the scoring, so a lot of mistakes weren’t called to deduct from a skater’s points. This can be reflected with a higher median score compared to the other GP events across both sexes.

Average Total Score vs. World Score

It is also important to understand that average total score is not a good predictor of a skater’s score at the World Championship. The graphs below show World Score on the x-axis and average Total Score on the y-axis by year and sex. If average Total Score was the sole predictor of World Score, we would see the graphs with only positive sloping lines; however, that is not the case. Someone with a higher average score could score lower than someone with a lower average score. That is why we need to look at other variables that could affect a skater’s scores throughout the season and at Worlds.

graphdata %>% 
  ggplot(aes(x = worldscore)) +
  geom_line(aes(y=tsavg, color = sex))+
  labs(x = 'World Score', y = 'Average Total Score', title = 'Average Total Score vs. World Score')+
  facet_wrap(~year)+
  theme_bw()

Setting Up Models

Data Splitting

Our first step before fitting any models is to split our data into 2 sets, the training and testing set. The training set we will use to fit our model onto and the testing set is used to see how well our models will perform with unseen data. I chose a 70/30 split, where 70% of the data will go into the training set and 30% into the testing set. I also stratified the split on the response variable, worldscore, so that we get an even distribution of the variable in both sets, which is especially useful since we have that big gap in scores between the 24th and 25th place at Worlds, as mentioned before.

set.seed(1012) # for reproducibility

# 70/30 split, stratify on world_rank
scores_split <- initial_split(data, prop = 0.7, strata = worldscore)

score_train <- training(scores_split)
score_test <- testing(scores_split)

Let’s check the dimensions for the training dataset:

dim(score_train)
[1] 223  19

And the testing dataset:

dim(score_test)
[1] 96 19

The training set has 223 observations while the testing set has 96. This looks about right for our 70/30 split.

Building Recipes

Now we will build a recipe with our predictor and response variables to use for the models we will create. We can make one universal recipe, since we will use the same predictors and response for each model. We want to use all of the variables in our dataset, except for the skater’s name, and have worldscore be our response variable. We also want to make sure we turn any nominal variables into dummy variables, which indicates the presence or absence of a certain level of the nominal variable with either a 0 or 1. Lastly, we will also normalize our data, since the ranges of spavg and fpavg vary. Below is our recipe:

# Create Recipe
score_recipe <- recipe(worldscore ~ ., data = score_train) %>% 
  step_rm('skater') %>% 
  step_dummy(all_nominal_predictors()) %>% 
  step_normalize()

K-Fold Cross Validation

Next, we will prepare our data for k-fold cross validation. K-fold cross validation will split our training data into k subset or folds. One fold will be set aside, while the models we create will be fit on to the remaining k-1 folds. Then the fitted models will be tested on the fold that was set aside. We will repeat these steps k times, making sure to have a different fold be set aside each time. In the end, we will take the average of the model performance metrics from the testing fold to see how well the model performs on the training data.

K-fold cross validation allows a more accurate evaluation of a model’s performance since we are fitting and evaluating the model on multiple different splits. We measure multiple performance metrics and take an average rather than just measuring one metric.

For our analysis, we will use 10 folds. Below, we are splitting our training data into 10 folds while also stratifying on our response variable, worldscore, to make sure there is a balanced distribution of the variable between the different folds.

score_folds <- vfold_cv(score_train, v = 10, strata = worldscore)

Model Fitting

Now we will build our model and fit them onto the data. I will be fitting 5 different types of models: K-nearest neighbors, linear regression, elastic net, random forest, and gradient-boosted tree. For each model, we will be following these steps:

  1. Set up the model, tuning for any parameters that are needed. Add the engine and specify regression mode if needed.

  2. Set up the workflow, adding the model and recipe.

  3. Create tuning grid if there are any tuning parameters. Specify the range and levels of these parameters.

  4. Tune the model with the workflow, cross-validation flows, and the tuning grid.

  5. Save the tuned models to a RDS file and later, load back in the saved files. This will help us save time from continuously rerunning any time-consuming models.

K-Nearest Neighbors

K-nearest neighbors is a non-parametric model that measures the distance between data points to make predictions. It selects k nearest data points to the new data point we are trying to predict and averages the data points to predict the outcome of the new data. To choose k, we will tune it and choose the best performing k from a range of 1 to 10 with 10 levels.

##K-nearest neighbors

#set up model, tuning for neighbors parameter
knn_model <- nearest_neighbor(neighbors = tune()) %>% 
  set_mode('regression') %>% 
  set_engine('kknn')

#set up workflow and add model and recipe
knn_workflow <- workflow() %>% 
  add_model(knn_model) %>% 
  add_recipe(score_recipe)

#set up grid
knn_grid <- grid_regular(neighbors(range = c(1, 10)), levels = 10)

#tune model
knn_tune <- tune_grid(
  object = knn_workflow,
  resamples = score_folds,
  grid = knn_grid
)

#save models
write_rds(knn_tune, file = 'tuned_models/knn.rds')

Linear Regression

Linear regression is one of the most simple models, creating a linear relationship between the response variable and the predictor variables. It is not very flexible but it is easy to use and understand. There are no parameters to tune, so after setting up our workflow, we can skip right into fitting the model onto the folds.

##Linear regression

#set up model
lm_model <- linear_reg() %>% 
  set_engine('lm')

#set up workflow
lm_workflow <- workflow() %>% 
  add_model(lm_model) %>% 
  add_recipe(score_recipe)

#no tuning for linear regression, so we will skip into fitting the model onto the cross validation folds
lm <- fit_resamples(lm_workflow, resamples = score_folds)

Elastic Net

Elastic Net combines the regularization techniques of Lasso regression (shrinking irrelevant variables to 0) and Ridge regression (shrinking some variables to smaller numbers but not 0). This will help us get rid of irrelevant variables and also fix any multicollinearity problems. The model has 2 parameters: mixture which tells us what proportion of Lasso regularization is used in the model (0 for only Ridge regression, 1 for only Lasso), and penalty which tells us the overall level of regularization in the model. We will tune these 2 parameters on a range of 0 to 1 for mixture and 0 to 3 for penalty, each with 10 levels.

##Elastic net

#set up model, tuning for penalty and mixture
en_model <- linear_reg(mixture = tune(), penalty = tune()) %>%
  set_engine("glmnet") %>% 
  set_mode("regression")

#set up workflow
en_workflow <- workflow() %>%
  add_model(en_model) %>% 
  add_recipe(score_recipe)

#set up grid
en_grid <- grid_regular(penalty(range = c(0,3), trans = identity_trans()), mixture(range = c(0,1)), levels = 10)

#tune model
en_tune <- tune_grid(
  object = en_workflow,
  resamples = score_folds,
  grid = en_grid
)

#save model
write_rds(en_tune, file = 'tuned_models/en.rds')

Random Forest

A Random Forest model creates multiple decision trees that are each trained on a random subset of training data with a random subset of predictors. The predictions of all the trees are averaged to make predictions on new data. The model was 3 parameters: mtry which tells us the number of predictors that are randomly sampled at each split, trees which tells us the number of trees we want to create in the Random Forest, and min_n which tells us the minimum number of observations in the data sample that is required for the tree to continue splitting. We are tuning these parameters, with mtry allowed a range of 1 to 17 (the total number of predictors we have), trees gets a range of 200 to 500, and min_n gets a range of 8 to 13.

## Random Forest

#set up model, tuning for mtry, trees, min_n
rf_model <- rand_forest(mtry = tune(),
                        trees = tune(),
                        min_n = tune()) %>% 
  set_engine('ranger', importance = 'impurity') %>% 
  set_mode('regression')

#set up workflow
rf_flow <- workflow() %>% 
  add_model(rf_model) %>% 
  add_recipe(score_recipe)

#set up grid
#rf_grid <- grid_regular(mtry(range = c(1, 17)),
                        #trees(range = c(200, 500)),
                        #min_n(range = c(8, 13)), levels = 10)

#tune model
#rf_tune <- tune_grid(
  #object = rf_flow,
  #resamples = score_folds,
  #grid = rf_grid)

#save model
#write_rds(rf_tune, file = 'tuned_models/rf.rds')

Gradient-Boosted Tree

Gradient-Boosted Trees sequentially build trees based on the residuals of a model, rather than the outcome variable. It adds new trees to update the residuals of the previous trees so that it eventually minimizes overall error. We will again tune the parameters mtry and trees with the same range, but this time we will tune learn_rate rather than min_n since it has a bigger impact on the performance of the model. We gave learn_rate a range of 0.01 to 0.1 with 10 levels for each of the parameters, which tells us the rate that the boosted tree will learn.

##Gradient-boosted tree

#set up model, tuning for mtry, tress, learn_rate
gbt_model <- boost_tree(mtry = tune(),
                        trees = tune(),
                        learn_rate = tune()) %>% 
  set_engine('xgboost') %>% 
  set_mode('regression')

#set up workflow
gbt_flow <- workflow() %>% 
  add_model(gbt_model) %>% 
  add_recipe(score_recipe)

#set up grid
gbt_grid <- grid_regular(mtry(range = c(1, 17)),
                         trees(range = c(200, 500)),
                         learn_rate(range = c(0.01, 0.1)),
                         levels = 10)

#tune model
gbt_tune <- tune_grid(
  object = gbt_flow,
  resamples = score_folds,
  grid = gbt_grid
)

#save model
write_rds(gbt_tune, file = 'tuned_models/gbt.rds')

Model Results

With all of our models competed and saved, lets load back in the saved results and analyze their performance.

#K Nearest Neighbors
knn <- read_rds(file = 'tuned_models/knn.rds')

#Linear model saved under lm variable

#Elastic Net
en <- read_rds(file = 'tuned_models/en.rds')

#Random Forest
rf <- read_rds(file = 'tuned_models/rf.rds')

#Boosted Trees
bt <- read_rds(file = 'tuned_models/gbt.rds')

To analyze the performances of the models, I will use Root Mean Square Error (RMSE) as my performance metric. RMSE is a common measure of a regression model’s performance, showing us how far away the model’s predicted value is from the data’s true value. A better performing model will have a lower RMSE, telling us that the predicted values are closer to the true values. The RMSE value we will be comparing is the average RMSE across the our cross-validation folds. We will begin by comparing the models we tuned and see which values for the parameters we tuned worked the best based on RMSE.

Best Parameters

K-Nearest Neighbors

Let’s take a look at which K-Nearest Neighbors model performed the best. Below, we can see a graph of the RMSE for each k value. The best performing k value is the one with the lowest RMSE, which looks to be k = 10

autoplot(knn, metric = 'rmse')

Elastic Net

Next, let’s take a look at the RMSE for the Elastic Net model. The x-axis shows the penalty, the different colored lines show the mixture, and the y-axis shows the RMSE. In general, when the penalty and mixture is higher, the RMSE is lower and the model performs better.

autoplot(en, metric = 'rmse')

Random Forest

Now, let’s look at the RMSE for the Random Forest model. The x-axis shows the mtry, the different colored lines show the number of trees, and the y-axis shows the RMSE. In general, when the value for mtry was higher, the RMSE is lower and the model performs better.

autoplot(rf, metric = 'rmse')

Gradient-Boosted Tree

Finally, let’s take a look at the RMSE for the Gradient-Boosted Tree model. The x-axis shows the mtry, the different colored lines show the number of trees, the different graphs show the learning rate, and the y-axis shows the RMSE. Across the parameters, there doesn’t seem to be a pattern of which level of the parameters performed the best.

autoplot(bt, metric = 'rmse')

Best Models

Now that we get a general sense of which parameters worked best, let’s compare the different types of models with each other. We will use the function show_best to choose the best model based on the lowest RMSE value. Below are the tables showing the best performing models for each type. The variable mean is the mean RMSE value across the cross-validation folds, and the variable std_err is the standard deviation of the RMSE values.

#K-nearest Neighbors
knn_best <- show_best(knn, metric = 'rmse', n =1)

knn_best %>%
  kable(caption = 'Best K-nearest Neighbors Model') %>% 
  kable_styling(full_width = F, position = "left")
Best K-nearest Neighbors Model
neighbors .metric .estimator mean n std_err .config
10 rmse standard 61.95858 10 1.856955 Preprocessor1_Model10
#Linear Regression
lm_best <- collect_metrics(lm)[1,]

lm_best %>%
  kable(caption = 'Linear Model') %>% 
  kable_styling(full_width = F, position = "left")
Linear Model
.metric .estimator mean n std_err .config
rmse standard 51.75774 10 2.882907 Preprocessor1_Model1
#Elastic Net
en_best <- show_best(en, metric = 'rmse', n =1)

en_best %>%
  kable(caption = 'Best Elastic Net Model') %>% 
  kable_styling(full_width = F, position = "left")
Best Elastic Net Model
penalty mixture .metric .estimator mean n std_err .config
3 1 rmse standard 52.52107 10 1.912929 Preprocessor1_Model100
#Random Forest
rf_best <- show_best(rf, metric = 'rmse', n =1)

rf_best %>%
  kable(caption = 'Best Random Forest Model') %>% 
  kable_styling(full_width = F, position = "left")
Best Random Forest Model
mtry trees min_n .metric .estimator mean n std_err .config
8 266 13 rmse standard 49.63054 10 2.311282 Preprocessor1_Model525
#Boosted Tree
bt_best <- show_best(bt, metric = 'rmse', n =1)

bt_best %>%
  kable(caption = 'Best Gradient-Boosted Tree Model') %>% 
  kable_styling(full_width = F, position = "left")
Best Gradient-Boosted Tree Model
mtry trees learn_rate .metric .estimator mean n std_err .config
11 500 1.096478 rmse standard 59.77649 10 2.639742 Preprocessor1_Model0640

We can also compare the other models visually by plotting their mean RMSE values and the standard deviation.The graph below for the mean RMSE and the standard deviation for the models on the y-axis and the different models on the x-axis.

#combine RMSE and standard deviation for each model into a dataframe
rmse <- rbind(knn_best %>% select(mean, std_err) %>% mutate(model = 'KNN'), 
              en_best%>% select(mean, std_err)%>% mutate(model = 'Elastic Net'),
              rf_best%>% select(mean, std_err)%>% mutate(model = 'Random Forest'),
              bt_best%>% select(mean, std_err)%>% mutate(model = 'Boosted Tree'),
              lm_best%>% select(mean, std_err)%>% mutate(model = 'Linear Reg.'))

rmse %>% 
  ggplot(aes(x = model, y = mean, ymin = mean - std_err, ymax = mean + std_err)) +
  geom_pointrange()+
  labs(title = 'RMSE and Standard Deviation for Each Model', x = 'Model', y = 'RMSE')+
  theme_bw()

The model with that performs the best is the Random Forest model, with the lowest mean RMSE and the lowest standard deviation. The Linear Regression model performs the next best, then Elastic Net, then the Gradient-Boosted Tree model, and finally the K-Nearest Neighbors model.

Best Model Results

Here is the best performing model with the values of the parameters. It is a Random Forest model with a mtry value of 8, 266 trees, and a min_n value of 13. The RMSE value for this model is about 49.63.

rf_best%>%
  kable(caption = 'Best Random Forest Model') %>% 
  kable_styling(full_width = F, position = "left")
Best Random Forest Model
mtry trees min_n .metric .estimator mean n std_err .config
8 266 13 rmse standard 49.63054 10 2.311282 Preprocessor1_Model525

We will now finalize the our workflow with the best model and fit it onto the training set. Below is a graph that shows which predictor variables are the most important to predicting worldscore in the Random Forest model. The higher the value of Importance, the more important it is to the model. We can see that fpavg is the most important variable, followed by spavg, and then if skater is male.

final_flow <- finalize_workflow(rf_flow, rf_best)
final_model <- fit(final_flow, data = score_train)

final_model %>% extract_fit_parsnip() %>% 
  vip() +
  theme_bw()

Testing the model

Now, we will fit the model onto the testing set and see how well the model performs on unseen data. Below is the RMSE value for the model on the testing set. The RMSE value is about 46.6, which is a better value than the RMSE from our training set. So, we can say that our model is performing well on the testing set.

final_test <- augment(final_model, new_data = score_test) 
final_test%>% 
  rmse(truth = worldscore, estimate = .pred)
# A tibble: 1 × 3
  .metric .estimator .estimate
  <chr>   <chr>          <dbl>
1 rmse    standard        46.6

We can also see a plot of the predicted values versus the real values below. The points are relatively close to the line, so our model is performing pretty well. We can also see 2 clusters of points, where the skater did not reach the free skate and has a lower score and where the skater finished both segments for a higher total score. For only short program scores, we can see that the model tends to overestimate them. On the other hand, scores that reflected 2 segments tend to be underestimated.

final_test %>% 
  ggplot(aes(x = .pred, y = worldscore))+
  geom_point()+
  geom_abline()+
  theme_bw()

Something interesting would be to see how the rankings on the competition would change based on the predicted scores. Below is a slopegraph that shows the rankings of the skaters at the 2023 World Championships based on their predicted scores versus their actual rankings from their actual scores.

Below is the women’s change in ranks. The rankings do change quite a bit, but we did get the same skaters in the top 5 rankings.

compare <- augment(final_model, new_data = data)
f_23 <- compare %>% 
  filter(sex == 'Female', year == 2023) %>% 
  select(skater, .pred)
f_23 <- f_23[order(f_23$.pred, decreasing = TRUE),] %>% 
  mutate(predrank = 1:nrow(.))
f23compare <- merge(worldmerged %>% filter(year == 2023), f_23, by = c('skater'))
f23compare <- f23compare%>% 
  mutate(worldrank = ifelse(worldrank > 24, worldrank -5, worldrank)) %>% 
  pivot_longer(cols = c(worldrank, predrank), names_to = 'type', values_to = 'rank')


newggslopegraph(dataframe = f23compare,
                Times = type,
                Measurement = rank,
                Grouping = skater,
                ReverseYAxis = TRUE,
                Title = '2023 Figure Skating Championships - Womens',
                SubTitle = 'Rank Based on Predicted Vs. Actual Scores',
                Caption = '')

Below is the graph for the men’s. The men’s is more accurate as we got 6 rankings correct and also have the same skater’s in the top 5.

m_23 <- compare %>% 
  filter(sex == 'Male', year == 2023) %>% 
  select(skater, .pred)
m_23 <- m_23[order(m_23$.pred, decreasing = TRUE),] %>% 
  mutate(predrank = 1:nrow(.))
m23compare <- merge(worldmerged %>% filter(year == 2023), m_23, by = c('skater'))
m23compare <- m23compare%>% 
  mutate(worldrank = ifelse(worldrank > 4, worldrank - 1 , worldrank))%>%
  mutate(worldrank = ifelse(worldrank > 10, worldrank -1, worldrank)) %>%
  mutate(worldrank = ifelse(worldrank > 18, worldrank -1, worldrank)) %>%
  mutate(worldrank = ifelse(worldrank > 26, worldrank -1, worldrank)) %>%
  pivot_longer(cols = c(worldrank, predrank), names_to = 'type', values_to = 'rank')


newggslopegraph(dataframe = m23compare,
                Times = type,
                Measurement = rank,
                Grouping = skater,
                ReverseYAxis = TRUE,
                Title = '2023 Figure Skating Championships - Mens',
                SubTitle = 'Rank Based on Predicted Vs. Actual Scores',
                Caption = '')

Predicting 2024 Worlds

We can also use our model to predict the scores for the 2024 World Championships. We will use the Random Forest model and use data from the regular competitions throughout the 2023-2024 season to predict the scores at 2024 Worlds. I got the list of skater’s entered from the ISU website and web-scrapped it into an Excel sheet. I did the same process as the other dataset to clean up the data. Since the model does not hold any data for the year of 2024, I put the year for the 2024 data as 2023. Some skaters also did not have any competition data this season, so I filled it in with any scores they had from previous seasons. If they didn’t have any data at all, I just gave them 0’s for scores. Below are the predictions for the Men’s and Women’s events:

#Read in 2024 Entries
entries <- read_xlsx("2024 entires.xlsx", sheet = '2024 Entries')
entries <- entries %>% 
  filter(is.na(Sub)) %>% 
  clean_names() %>% 
  select(skater) %>% 
  mutate(skater = str_to_title(skater)) %>% 
  mutate(year = as.factor(2023))

#Clean up data for scores in 2024
score24 <- clean_names(score24)
score24$skater <- gsub('[0-9]', '', score24$skater)
score24$skater <- str_squish(score24$skater)

#get average scores for 2024
avg2 <- score24 %>% 
  group_by(skater) %>% 
  mutate(spavg = mean(sp_score,na.rm = TRUE)) %>% 
  mutate(fpavg = mean(fs_score, na.rm=TRUE)) %>% 
  select(skater, competition, spavg, fpavg, sex)

#pivot table to match original dataset
avg2$truth <- 1
pivoted2 <- avg2 %>% 
  pivot_wider(names_from = competition, values_from = truth)
pivoted2[is.na(pivoted2)] <- 0

#merge together 
worlds24 <- left_join(entries, pivoted2, by = c('skater'))

#fill in 0 for missing competition variables
worlds24$GPITA <- as.factor(0)
worlds24$GPRUS <- as.factor(0)
worlds24$GPGBR <- as.factor(0)
worlds24$OLY <- as.factor(0)

#factor competition variables
for (i in 6:18) {
  if (is.numeric(worlds24[[i]])) {  # Check if the column is numeric
    worlds24[is.na(worlds24[[i]]), i] <- 0  # Replace NA values with 0
    worlds24[[i]] <- factor(worlds24[[i]])  # Convert column to factor
  }
}

#fill in missing sex variables
worlds24[c(7,21,22),5] <- 'Female'
worlds24[c(36,46, 65,73),5] <- 'Male'

#fill in missing score variables with latest scores or 0
for (i in c(7, 21, 22, 36,46, 65,73)) {
  if (i == 7) {
    worlds24[7, c(3,4)] <- data[93, c(4,5)]
  } else if (i == 46) {
    worlds24[46, c(3,4)] <- data[299, c(4,5)]
  } else if (i == 73) {
    worlds24[73, c(3,4)] <- data[130, c(4,5)]
  } else {
    worlds24[i, c(3,4)] <- 0
  }
}

#predictions
table <- augment(final_model, new_data = worlds24)

#female table
female <- table %>% 
  filter(sex == 'Female') %>% 
  select(skater, .pred) 
female[order(female$.pred, decreasing = TRUE),] %>% 
  mutate(rank = 1:nrow(.)) %>%
  kable(caption = '2024 Predictions - Women') %>% 
  kable_styling(full_width = F, position = "left")%>% 
  scroll_box(width = "100%", height = "200px")
2024 Predictions - Women
skater .pred rank
Kaori Sakamoto 218.92409 1
Loena Hendrickx 210.59419 2
Isabeau Levito 208.09762 3
Chaeyeon Kim 192.58412 4
Hana Yoshida 187.45573 5
Nina Pinzarrone 187.36935 6
Livia Kaiser 181.09405 7
Mone Chiba 179.71017 8
Amber Glenn 179.55052 9
Anastasiia Gubanova 177.64526 10
Niina Petrõkina 174.87683 11
Sarina Joos 172.70468 12
Haein Lee 171.98760 13
Kimmy Repond 166.77064 14
Ekaterina Kurakova 166.13705 15
Tzu-Han Ting 163.24541 16
Lorine Schild 163.17047 17
Madeline Schizas 160.49636 18
Julia Sauter 146.85595 19
Josefin Taljegård 143.56948 20
Nataly Langerbaur 142.89156 21
Eliska Brezinova 141.67570 22
Kristina Isaev 139.64798 23
Young You 135.95691 24
Sofja Stepchenko 131.24583 25
Olga Mikutina 128.59074 26
Nina Povey 125.92183 27
Mariia Seniuk 125.64937 28
Alexandra Feigin 122.10273 29
Nella Pelkonen 121.81301 30
Anastasia Gozhva 104.83027 31
Meda Variakojyte 66.13431 32
Anastasia Gracheva 66.13431 33
Mia Caroline Risa Gomez 61.64168 34
Vanesa Selmeková 56.76028 35
#male table
male <- table %>% 
  filter(sex == 'Male') %>% 
  select(skater, .pred) 
male[order(male$.pred, decreasing = TRUE),] %>% 
  mutate(rank = 1:nrow(.)) %>%
  kable(caption = '2024 Predictions - Men') %>% 
  kable_styling(full_width = F, position = "left")%>% 
  scroll_box(width = "100%", height = "200px")
2024 Predictions - Men
skater .pred rank
Ilia Malinin 293.94883 1
Yuma Kagiyama 286.89809 2
Shoma Uno 280.90844 3
Adam Siao Him Fa 278.80246 4
Kao Miura 273.11882 5
Jason Brown 253.04533 6
Gabriele Frangipani 252.82965 7
Mikhail Shaidorov 250.89532 8
Aleksandr Selevko 250.82248 9
Nika Egadze 250.55149 10
Lukas Britschgi 249.62953 11
Boyang Jin 244.90687 12
Junhwan Cha 242.84779 13
Georgiy Reshtenko 228.41120 14
Vladimir Litvintsev 227.96176 15
Vladimir Samoilov 226.46857 16
Mark Gorodnitsky 216.60233 17
Roman Sadovsky 213.09935 18
Camden Pulkinen 212.89196 19
Nikolaj Memola 211.51361 20
Adam Hagara 208.12472 21
Wesley Chiu 204.16965 22
Sihyeong Lee 196.53445 23
Nikita Starostin 194.85654 24
Luc Economides 191.14220 25
Andreas Nordebäck 190.70275 26
Deniss Vasiljevs 190.16392 27
Ivan Shmuratko 179.30869 28
Donovan Carrillo 177.36139 29
Tomás Guarino Sabaté 176.09272 30
Maurizio Zandrón 173.39120 31
Aleksandr Vlasenko 128.38718 32
Davidé Lewton Brain 120.96139 33
Valtter Virtanen 94.97995 34
Burak Demirboga 90.69672 35
Edward Appleby 89.69571 36
Jari Kessler 88.47896 37
Alexander Zlatkov 82.61716 38
Semen Daniliants 68.31597 39
Hyungyeom Kim 68.31597 40

Conclusion

After fitting different models onto our training data with cross validation, we found that a Random Forest model was best at predicting the scores of skaters at the World Championships. This makes since typically Random Forests are flexible models that have higher predictive power. However, there is definitely room to improvement. We found that there was overestimation for scores that only came from the short program while underestimation from scores that came from both segments.

The worst performing was the K-Nearest Neighbors model. While it is easy to use and understand, the model does not perform well when there are a lot of variables and is sensitive to outliers, imbalanced data and overfitting. Since we have a lot of predictor variables and our score variables can have an imbalanced distribution, it makes sense that K-Nearest Neighbors did not do too well.

For the future, it would be interesting to do this prediction again but with variables that could describe the physical state of a skater or take into account the previous season’s scores. For example, in our 2024 Women’s predictions, we predicted Young You to be 24th place, but she has been making a comeback after a bad injury and after seeing her most recent domestic competition, I would predict she could be top 10. Since we also had that discrepancy in scores because of the limit of skaters that are allowed in the free skate, it would also be interesting to add a component that showed how many competitors there are or something to weigh how likely they are to get into the free skate. The overestimation for the one-segment scores come from we have data where a skater’s free skate score can be really high. But then suddenly for Worlds, their free skate score is 0. More competitors could also make it harder to make it to the free skate. It would be interesting to find a way to account for this to make the model more accurate in the score predictions.

Wakaba Higuchi - Women’s Figure Skating
Wakaba Higuchi - Women’s Figure Skating

Sources

SkatingScores, skatingscores.com/. Accessed 17 Mar. 2024.

“Figure Skating - International Skating Union.” International Skating Union, https://www.isu.org/figure-skating. Accessed 17 Mar. 2024.